Predicate Devices

Reference Devices

K210632, K071964, K182888, K193109, K173635

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The summary explicitly states that ART-Plan supports "Al-based contouring" and "deep-learning based automatic segmentation" and "deep-learning based synthetic CT-generation".

Therapeutic

No.
The device is a software application designed for image processing and visualization in radiation treatment planning, not for delivering therapeutic treatment itself.

Diagnostic

No

This device is intended for treatment planning in cancer patients receiving radiation therapy, focusing on image display, visualization, contouring, and registration for treatment planning systems, rather than diagnosing a disease or condition.

SaMD (Software as a Medical Device)

Yes

The device is described as a "software application" and its functionalities are entirely software-based, involving image processing, AI-based contouring, registration, and pseudo-CT generation. There is no mention of accompanying hardware components that are part of the medical device itself.

IVD (In Vitro Diagnostic)

Based on the provided information, ART-Plan is not an In Vitro Diagnostic (IVD) device.

Here's why:

IVD Definition: In Vitro Diagnostic devices are used to examine specimens (like blood, urine, or tissue) taken from the human body to provide information for diagnosis, monitoring, or screening.
ART-Plan's Function: ART-Plan is a software application that processes and visualizes medical images (CT, PET-CT, MR, etc.) taken from the patient. It assists in radiation treatment planning by providing tools for image display, registration, segmentation (contouring), and pseudo-CT generation. It does not analyze biological specimens.

Therefore, ART-Plan falls under the category of medical imaging software or radiation therapy planning software, not In Vitro Diagnostics.

PCCP (Predetermined Change Control Plan) Authorized

No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.

Overview

Intended Use / Indications for Use

ART-Plan is indicated for cancer patients for whom radiation treatment has been planned. It is intended to be used by trained medical professionals including, but not limited to, radiation oncologists, dosimetrists, and medical physicists.

ART-Plan is a software application intended to display and visualize 3D multi-modal medical image data. The user may import, define, display, transform and store DICOM3.0 compliant datasets (including regions of interest structures). These images, contours and objects can subsequently be exported/distributed within the system, across computer networks and/or to radiation treatment planning systems. Supported modalities include CT, PET-CT, CBCT, 4D-CT and MR images.

ART-Plan supports AI-based contouring on CT and MR images and offers semi-automatic and manual tools for segmentation.

To help the user assess changes in image data and to obtain combined multi-modal image information, ART-Plan allows the registration of anatomical and functional images and display of fused and non-fused images to facilitate the comparison of patient image data by the user.

With ART-Plan, users are also able to generate, visualize, evaluate and modify pseudo-CT from MRI images.

Product codes (comma separated list FDA assigned to the subject device)

QKB, LLZ, MUJ

Device Description

The ART-Plan application is comprised of two key modules: SmartFuse and Annotate, allowing the user to display and visualize 3D multi-modal medical image data. The user may process, render, review, store, display and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities cover static and gated CT (computerized tomography including CBCT and 4D-CT), PET (positron emission tomography) and MR (magnetic resonance).

Compared to ART-Plan v1.6.1 (primary predicate), the following additional features have been added to ART-Plan v1.10.0:

an improved version of the existing automatic segmentation tool
automatic segmentation on more anatomies and organ-at-risk
image registration on 4D-CT and CBCT images .
automatic segmentation on MR images .
generate synthetic CT from MR images
a cloud-based deployment

The ART-Plan technical functionalities claimed by TheraPanacea are the following:

Proposing automatic solutions to the user, such as an automatic delineation, automatic multimodal image fusion, etc. towards improving standardization of processes/ performance / reducing user tedious / time consuming involvement.
Offering to the user a set of tools to assist semi-automatic delineation, semi-automatic registration towards modifying/editing manually automatically generated structures and adding/removing new/undesired structures or imposing user-provided correspondences constraints on the fusion of multimodal images.
Presenting to the user a set of visualization methods of the delineated structures, and registration fusion maps.
Saving the delineated structures / fusion results for use in the dosimetry process.
Enabling rigid and deformable registration of patients images sets to combine information contained in different or same modalities.
Allowing the users to generate, visualize, evaluate and modify pseudo-CT from MRI images.

ART-Plan offers deep-learning based automatic segmentation for the following localizations:

head and neck (on CT images) .
thorax/breast (for male/female and on CT images)
abdomen (on CT images and MR images)
pelvis male(on CT images and MR images)
pelvis female (on CT images)
brain (on CT images and MR images)

ART-Plan offers deep-learning based synthetic CT-generation from MR images for the following localizations:

pelvis male .
brain

Mentions image processing

Yes

Mentions AI, DNN, or ML

Yes

Input Imaging Modality

CT, PET-CT, CBCT, 4D-CT, MR

Anatomical Site

Head and Neck, Thorax/Breast, Abdomen, Pelvis (male/female), Brain

Indicated Patient Age Range

Not Found

Intended User / Care Setting

Trained medical professionals including, but not limited to, radiation oncologists, dosimetrists, and medical physicists.
The device is intended to be used in a radiation therapy clinical setting, by trained professionals only.

Description of the training set, sample size, data source, and annotation protocol

Our training, validation and test cohorts are built from real-world retrospective data which were initially used for treatment of cancer patients. For the structures of a given anatomy for a given modality (MR or CT), two non-overlapping data sets were separated: the test patients (number selected based on thorough literature review and statistical power) and the train data. We make sure that those sets are non-overlapping and further split the train cases into train and validation sets and ensure enough train cases for the machine learning models to converge and achieve good performances of the validation set.

Sample Size:
Training: 299142 samples (0.8 of total)
Validation: 75018 samples (0.2 of total)
Total: 374160 samples

The total number of patients used for training (8736) is lower than the number of samples (374160). This is linked to the fact that one patient can be associated with more images (e.g. CT, MR) and that each image (anatomy) has the delineation of several structures (OARs and lymph nodes) which increases the number of samples used for training and validation.

Demographic distribution including gender, age and ethnicity
All data used for training of the models have been pseudo-anonymised by the centers providing data before transfer. Around 80% of the data used for training contain information on gender and age of the patients. In terms of gender, around 44% and 56% of our data (that contains this information) are from female and male patients, respectively. In comparison, in 2020 according to the Global Cancer Observatory, 48% and 52% of the cancer patients were female and male, respectively.

In terms of age, our data follows the same trend observed and reported in the US (SEER NIH), UK (Cancer Research UK) and worldwide (Global Cancer Observatory) for cancer incidence according to age, with more than 95% of the data coming from patients between 20 and 85 years old. Our data has a slight overrepresentation (8% points) for the ages between 54 and 60 years old, at the cost of a slight underrepresentation of patients in the age range between 20-34 (1.5% points) and above 85 (6.5% points) years old. In addition, following the general global (incl US) trend, our data also depicts a steep rise in the incidence rate from in the age group of 55-64 years old, with a median age of 63 years old (as compared to 66 years old in the US).

Although this information is not exhaustive, this analysis shows that the demographic distribution in terms of age and gender of the data used for training and validation of the models are well aligned with the incidence cancer statistics found for instance in US, UK and globally. This comes from the fact that real clinical data provided by medical facilities without any selection criteria (i.e. no discrimination or selection has been applied to the cases retrieved), leading to the demographic distribution including gender and age across the data is representative of the distribution in the clinic and thus of the cancer patient population in general.

An exception is noted for following models that are gender-dependent:

100% of pelvis images for male pelvis model for automatic annotation are male patients
100% of pelvis images for female pelvis model for automatic annotation are female patients
100% of breast images for the breast automatic annotation are female patients
100 % of pelvis images for automatic synthetic-CT generation are male patients

No pseudo-anonymized data included any information on the ethnicity.

On the "truthing" and data collection process:
The contouring guidelines followed to produce the contours were confirmed with the centers which provided the data. Our truthing process includes a mix of data created by different delineators (clinical experts) and assessment of intervariability, ground truth contours provided by the centers and validated by a second expert of the center, and qualitative evaluation and validation of the contours. This process ensures that the data used for training and testing can be considered representative of the delineation practice across centers and following international guidelines.

Description of the test set, sample size, data source, and annotation protocol

Our training, validation and test cohorts are built from real-world retrospective data which were initially used for treatment of cancer patients. For the structures of a given anatomy for a given modality (MR or CT), two non-overlapping data sets were separated: the test patients (number selected based on thorough literature review and statistical power) and the train data. We make sure that those sets are non-overlapping and further split the train cases into train and validation sets and ensure enough train cases for the machine learning models to converge and achieve good performances of the validation set.

Annotation protocol for test set: The contouring guidelines followed to produce the contours were confirmed with the centers which provided the data. Our truthing process includes a mix of data created by different delineators (clinical experts) and assessment of intervariability, ground truth contours provided by the centers and validated by a second expert of the center, and qualitative evaluation and validation of the contours. This process ensures that the data used for training and testing can be considered representative of the delineation practice across centers and following international guidelines.

Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)

Study Type: Usability testing, performance testing, regression testing, qualitative and quantitative validation of fusion performances, clinical performance comparisons.
Sample Size: Not explicitly stated for all individual studies, but the overall training sample size is 374160 images from 8736 patients. For auto segmentation model acceptance, "test patients (number selected based on thorough literature review and statistical power)".
Key Results:

Usability: All usability tests were passed, demonstrating compliance with IEC 62366-1:2015+AMD1:2020.
Auto Segmentation Performance:
- Acceptable contours for concerned structures provided by Annotate module.
- Mean Dice Similarity Coefficient (DSC) compared to an inter-expert variability benchmark for various organs.
- Qualitative evaluation by clinicians considered acceptable for clinical use without or with minor modifications (A+B % above or equal to 85%).
- Brain MRI autosegmentation: Some organs did not meet 0.80 DSC, but qualitative evaluation ensured clinical acceptability after improvements in v1.10.0.
- Thorax, Gyneco, Pelvis MRI models passed acceptance criteria.
- Equivalent performances demonstrated between non-US and US population for auto-segmentation on Thorax US data.
- Performance on Pediatric images showed high generalizability, with most structures meeting acceptance criteria, however, the device is not claimed for pediatric patients.
- Non-inferiority to predicate devices (MIM/ContourProtege AI) was demonstrated, with ART-Plan v1.10.0 offering significantly more organs.
Pseudo-CT Generation Performance:
- Pelvis and Brain pseudo-CT generation demonstrated non-inferiority for treatment planning in terms of dosimetric measures compared to CT-based planning.
- Met acceptance criteria derived from clinical practice and literature review (median 2%/2mm gamma passing ≥95%, median 3%/3mm gamma passing ≥99.0%, mean dose deviation ≤2% in ≥88% of patients).
- Performed at least as good as two FDA cleared devices for pseudo-CT generation.
Fusion Performances:
- Rigid and Deformable fusion algorithms in SmartFuse passed performed tests and provided valid results for clinical use in radiotherapy across various clinical use cases (e.g., CT injected to CT, CT-PET to CT, MRI to planning CT, CTs to planning MRIs, MRI replanning, CT-based treatment replanning).
Regression Testing: Demonstrated equivalence between versions and clinically acceptable contours for new anatomies and updated models.
System Verification and Validation: All tests passed.
Software Verification and Validation: Software considered a "major" level of concern with all tests passed.
No animal studies or clinical studies were conducted as part of the submission to prove substantial equivalence.

Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)

Dice Similarity Coefficient (DSC):

DSC (mean)≥ 0.8 (AAPM acceptance criteria)
DSC (mean)≥ 0.54 or DSC (mean)≥ mean (DSC inter-expert) + 5% (inter-expert variability acceptance criteria)

Qualitative Evaluation:

A+B % above or equal to 85% (A: acceptable for clinical use without modification, B: acceptable for clinical use after minor modifications/corrections, C: requires major modifications)

Pseudo-CT Generation:

A median 2%/2mm gamma passing criteria of ≥95%
A median 3%/3mm gamma passing criteria of ≥99.0%
A mean dose deviation (pseudo-CT compared to standard CT) of ≤2% in ≥88% of patients

Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.

K202700

Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.

K210632, K071964, K182888, K193109, K173635

Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).

Not Found

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).

Summary

0

Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in a stacked format.

TheraPanacea % Edwin Lindsay QA/RA consultant Pépinière Cochin Paris Santé, 29 rue du Faubourg Saint-Jacques Paris. 75014 FRANCE

Re: K220813

Trade/Device Name: ART-Plan Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management and Processing System Regulatory Class: Class II Product Code: QKB Dated: March 16, 2022 Received: March 21, 2022

Dear Edwin Lindsay:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

1

devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Julie Sullivan, Ph.D. Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

for

2

Indications for Use

510(k) Number (if known) K220813

Device Name ART-Plan

Indications for Use (Describe)

ART-Plan is indicated for cancer patients for whom radiation treatment has been planned. It is intended to be used by tramed medical professionals including, but not limited to, radiation oncologists, dosimetrists, and medical physicists.

ART-Plan is a software application intended to display and visualize 3D multi-modal medical image data. The user may import, define, display, transform and store DICOM3.0 compliant datasets (including regions of interest structures). These images, contours and objects can subsequently be exported/distributed within the system, across computer networks and/or to radiation treatment planning systems. Supported modalities include CT. PET-CT. CBCT, 4D-CT and MR images.

ART-Plan supports Al-based contouring on CT and MR images and offers semi-automatic and manual tools for segmentation.

To help the user assess changes in image data and to obtain combined multi-modal image information, ART-Plan allows the registration of anatomical and functional images and display of fused and non-fused images to facilitate the comparison of patient image data by the user.

With ART-Plan, users are also able to generate, visualize, evaluate and modify pseudo-CT from MRI images.

Type of Use (Select one or both, as applicable)

☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

This 510(k) Summary is submitted in accordance with 21 CFR Part 807, Section 807.92.

Submitter's Name:

TheraPanacea

Submitter's Address:

Pépinière Paris Santé Cochin 29 rue du Faubourg Saint-Jacques 75014 Paris France

Telephone: +33 9 62 52 78 19

Establishment Registration Number:

3019834893

Contact Person:

Edwin Lindsay

Telephone +44 (0) 7917134922

Date Prepared:

16 Mar 2022

Below summaries the Device Classification Information regarding the TheraPanacea ART-Plan:

Primary Product Code:

| Regulation
Number | Device | Device
Class | Product
Code | Classification
Panel |
|----------------------|------------------------------------------------------|-----------------|-----------------|-------------------------|
| 892.2050 | Medical image
management and
processing system | Class II | QKB | Radiology |

Device Trade Name:

ART-Plan

Device Common Name:

ART-Plan

Intended Use:

Page 1 of 28

4

ART-Plan is a software for multi-modal visualization, contouring and processing of 3D images of cancer patients for whom radiotherapy treatment has been prescribed.

It allows the user to view, create and modify contours for the regions of interest. It also allows to generate automatically, and based on medical practices, the contours for the organs at risk and healthy lymph nodes and to register combinations of anatomical and functional images. Contours and images require verifications, potential modifications, and subsequently the validation of a trained user with professional qualifications in anatomy and radiotherapy before their export to a Treatment Planning System.

ART-Plan offers the following visualization, contouring and manipulation tools to aid in the preparation of radiotherapy treatment:

Multi-modal visualization and rigid- and deformable registration of anatomical and O functional images such as CT, MR, PET-CT, 4D-CT and CBCT
O Display of fused and non-fused images to facilitate the comparison and delineation of image data by the user
O Manual modification and semi-automatic generation of contours for the regions of interest
Automatic generation of contours for organs at risk and healthy lymph nodes, based 0 on medical practices, on medical images such as CT and MR images.
Generation of pseudo-CT for supported anatomies 0

The device is intended to be used in a radiation therapy clinical setting, by trained professionals only.

Indications for Use:

ART-Plan is indicated for cancer patients for whom radiation treatment has been planned. It is intended to be used by trained medical professionals including, but not limited to, radiologists, radiation oncologists, dosimetrists, and medical physicists.

ART-Plan is a software application intended to display and visualize 3D multi-modal medical image data. The user may import, define, display, transform and store DICOM3.0 compliant datasets (including regions of interest structures). These images, contours and objects can subsequently be exported/distributed within the system, across computer networks and/or to radiation treatment planning systems. Supported modalities include CT, PET-CT, CBCT, 4D-CT and MR images.

ART-Plan supports Al-based contouring on CT and MR images and offers semi-automatic and manual tools for seqmentation.

To help the user assess changes in image data and to obtain combined multi-modal image information, ART-Plan allows the registration of anatomical and functional images and display of fused and non-fused images to facilitate the comparison of patient image data by the user.

With ART-Plan, users are also able to generate, visualize, evaluate and modify pseudo-CT from MRI images.

5

Summary of Substantial Equivalence:

The following predicate devices have been that the ART-Plan can claim equivalence with and these are detailed below

General Comparison

	General Information
Property	Proposed Device
ART-Plan v1.10.0	Primary
Predicate
ART-Plan
v1.6.1	Reference
device
Contour
ProtégéAl	Reference
device
MIM 4.1	Reference
device
MRCAT Pelvis	Reference
device
MRCAT Brain	Reference
device
Syngo.via RT
Image Suite	Comment
Common
Name	Radiological image
processing software for
radiation therapy	Radiological image
processing software
for radiation therapy	Radiological
Image
Processing
Software For
Radiation
Therapy	System, image
processing,
radiological	System,
Planning,
Radiation
Therapy
Treatment	System,
Planning,
Radiation
Therapy
Treatment	System,
Planning,
Radiation
Therapy
Treatment	N/A
Device
Manufacturer	TheraPanacea	TheraPanacea	MIM Software,
Inc	MIMvista Corp
(now MIM
Software Inc)	Philips Medical
Systems	Philips Medical
Systems	Siemens
Medical
Solutions USA,
Inc.	N/A
510k	N/A	K202700	K210632	K071964	K182888	K193109	K173635	N/A
Device
Classification	II	II	II	II	II	II	II	N/A
Primary
Product Code	QKB	QKB	QKB	LLZ	MUJ	MUJ	MUJ	The primary product code
is QKB "Radiological
Image Processing
Software For Radiation
Therapy" as the software
uses Al algorithms and is
intended for radiation
therapy, just like the
primary predicate device
Secondary
Product Code	LLZ, MUJ	LLZ	-	-	-	-	LLZ	As secondary product
code:
LLZ (System, Image
Processing,
Radiological) was
included as the
software is used in
								image processing and
some predicates use it
as primary or
secondary product
code:
MUJ (System,
Planning, Radiation
Therapy Treatment)
was includes as it is a
software used in the
planning of
radiotherapy
treatment and some of
the reference devices
use it as their primary
code
Target
Population	Any patient type for
whom relevant modality
scan image data is
available	Any patient type for
whom relevant
modality scan data is
available	Not stated	Not stated	Any patient with
soft tissue
cancers in the
pelvic region for
whom
radiotherapy
treatment has
been planned	Any patient with
primary and
metastatic brain
tumor for whom
radiotherapy
treatment has
been planned	Not stated	The proposed device has
identical target
populations to the primary
and reference devices.
Environment	Hospital	Hospital	Hospital	Hospital	Hospital	Hospital	Hospital	The proposed device
and predicates have
identical target
environments
Intended Use/
Indication for
Use	Intended Use
ART-Plan is a software
for multi-modal
visualization, contouring
and processing of 3D
images of cancer
patients for whom
radiotherapy treatment
has been prescribed.
It allows the user to
view, create and modify
contours for the regions
of interest. It also allows
to generate
automatically, and
based on medical	Intended Use
ART-Plan is a
software designed to
assist the contouring
process of the target
anatomical regions
on 3D-images of
cancer patients for
whom radiotherapy
treatment has been
planned.
The SmartFuse
module allows the
user to register
combinations of
anatomical and	Intended Use
Contour
ProtégéAl is an
accessory to
MIM software
used for the
contouring of
anatomical
structures in
imaging data
using
machine-learnin
g-based
algorithms
automatically.
Appropriate	Intended Use
MIM 4.1
(SEASTAR)
software is
intended for
trained medical
professionals
including, but not
limited to,
radiologists,
oncologists,
physicians,
medical
technologists,
dosimetrists and
physicists.	Intended Use:
MRCAT imaging
is
intended to
provide the
operator with
information of
tissue
properties
for radiation
attenuation
estimation
purposes
in photon
external beam
radiotherapy	Intended Use:
MRCAT imaging
is intended to
provide the
operator with
information of
tissue
properties for
radiation
attenuation
estimation
purposes in
photon external
beam
radiotherapy	Intended use:
Not available in
the summary
Indication for
use:
syngo.via RT
Image Suite is a
3D and 4D
image
visualization,
multimodality
manipulation
and
contouring tool
that helps the	The intended use and
indications for use of
the proposed device,
ART-Plan v1.10.0 and
the primary predicate
ART-Plan v1.6.1 are
similar as they are
both intended for
medical image
registration and
segmentation in the
context of radiotherapy
treatment planning:
they allow multi-modal
and mono-modal rigid
practices, the contours	functional images and	image	MIM 4.1
(SEASTAR) is a
medical image
and information
management
system that is
intended to
receive, transmit,
store, retrieve,
display, print and
process digital
medical images,
as well as create,
display and print
reports from those
images. The
medical
modalities of
these medical
imaging systems
include, but are
not limited to, CT,
MRI, CR, DX,
MG, US, SPECT,
PET and XA as
supported by
ACR/NEMA
DICOM 3.0.	treatment
planning.	treatment
planning.	preparation and
response
assessment of
treatments such
as, but not
limited to those
performed with
radiation (for
example,
Brachytherapy,
Particle
Therapy,
External
Beam Radiation
Therapy).
It provides tools
to efficiently
view existing
contours,
create, edit,
modify, copy
contours of
regions of
the body, such
as but not
limited to, skin
outline, targets
and
organs-at-risk. It
also provides
functionalities to
create and
modify simple
treatment plans.
Contours,
images and
treatment plans
can
subsequently be
exported to a
Treatment
Planning
System.
The software
combines
following digital	deformable registration
for the same modalities
of images (CT, MR, PET
they allow automatic
segmentation of
organs-at-risk and lymph
nodes on injected and
non-injected CT images
using deep learning
algorithms
they allow the import,
manipulation,
visualisation, generation
and the export of DICOM
images
The intended for the
proposed device has
been adapted to
provide a more specific
description of the
proposed device but
does not represent a
new intended use,
except for the
additional claims for
the proposed device
as compared to the
primary predicate as:

it includes an improved
version of the existing
automatic segmentation
tool as compared to the
one of ART-Plan v1.6.1.
it allows automatic
segmentation on more
anatomies and
organ-at-risk | |
| for the organs at risk | display them with | visualization
software must
be used to
review and, if
necessary, edit
results
automatically
generated by
Contour
ProtégéAI.
Contour
ProtégéAI is not
intended to
detect or
contour lesions. | MIM 4.1
(SEASTAR) provides the user
with the means to
display, register
and fuse medical
images from
multiple
modalities.
Additionally, it
evaluates cardiac
left ventricular
function and
perfusion,
including left
ventricular | Indications for
Use: | Indications for
use: | | | |
| and healthy lymph | fused and non-fused | | | | | | | |
| nodes and to register | displays to facilitate | | | | | | | |
| combinations of | the comparison and | | | | | | | |
| anatomical and | delineation of image | | | | | | | |
| functional images. | data by the user. | | | MRCAT Pelvis
is indicated for
radiotherapy
treatment
planning of soft
tissue cancers
in the pelvic
region. | MRCAT is
indicated for
radiotherapy
treatment
planning for
primary and
metastatic brain
tumor patients | | | |
| Contours and images | The images created | | | | | | | |
| require verifications, | with rigid or elastic | | | | | | | |
| potential modifications, | registration require | | | | | | | |
| and subsequently the | verifications, potential | | | | | | | |
| validation of a trained | modifications, and | | | | | | | |
| user with professional | then the validation of | | | | | | | |
| qualifications in | a trained user with | | | | | | | |
| anatomy and | professional | | | | | | | |
| radiotherapy before | qualifications in | | | | | | | |
| their export to a | anatomy and | | | | | | | |
| Treatment Planning | radiotherapy. | Indications for
Use | | | | | | |
| System. | With the Annotate | Trained medical | | | | | | |
| | module, users can | professionals | | | | | | |
| ART-Plan offers the | edit manually and | use Contour | | | | | | |
| following visualization, | semi-automatically | ProtégéAI as a | | | | | | |
| contouring and | the contours for the | tool to assist in | | | | | | |
| manipulation tools to | regions of interest. It | the automated | | | | | | |
| aid in the preparation of | also allows to | processing of | | | | | | |
| radiotherapy treatment: | generate | digital medical | | | | | | |
| | automatically, and | images of | | | | | | |
| - Multi-modal | based on medical | modalities CT | | | | | | |
| visualization and rigid- | practices, the | and MR, as | | | | | | |
| and deformable | contours for the | supported by | | | | | | |
| reqistration of | organs at risk and | ACR/NEMA | | | | | | |
| anatomical and | healthy lymph nodes | DICOM 3.0. In | | | | | | |
| functional images such | on CT images. | addition. | | | | | | |
| as CT, MR, PET-CT, | The contours created | Contour | | | | | | |
| 4D-CT and CBCT | automatically. | ProtégéAI | | | | | | |
| - Display of fused and | semi-automatically or | supports the | | | | | | |
| non-fused imaqes to | manually require | following | | | | | | |
| facilitate the | verifications, potential | indications: • | | | | | | |
| | modifications, and | Creation of | | | | | | |
| comparison and | then the validation of | contours using | | | | | | |
| delineation of image | a trained user with | machine-learnin | | | | | | |
| data by the user | professional | g algorithms for | | | | | | |
| - Manual modification | qualifications in | applications | | | | | | |
| and semi-automatic | anatomy and | including, but | | | | | | |
| generation of contours | radiotherapy. | not limited to, | | | | | | |
| | | ventricular | | | | | | |

6

Page 4 of 28

7

8

| for the regions of interest

Automatic generation of contours for organs at risk and healthy lymph nodes, based on medical practices, on medical images such as CT and MR images.
Generation of pseudo-CT for supported anatomies | The device is intended to be used in a clinical setting, by trained professionals only.

Indications for Use
ART-Plan is intended to be used by trained medical professionals including, but not limited to, radiologists, radiation oncologists, dosimetrists and physicists.
ART-Plan is a software application intended to display and visualize 3D multi-modal medical image data. The user may process, render, review, store, display and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities include static and gated CT, PET, and MR.
ART-Plan allows the user to register combinations of anatomical and functional images and display them with fused and non-fused displays to facilitate the comparison of image data by the user. PET images should not be | quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management. • Segmenting normal structures across a variety of CT anatomical locations. • And segmenting normal structures of the prostate, seminal vesicles, and urethra within T2-weighted MR images. Appropriate image visualization software must be used to review and, if necessary, edit results automatically generated by Contour ProtégéAI. | end-diastolic volume, end-systolic volume, and ejection fraction. The_Region of Interest (ROI) feature reduces the time necessary for the user to define objects in medical image volumes by providing an initial definition of object contours. The objects include, but are not limited to, tumors and normal tissues.

MIM 4.1 (SEASTAR) provides tools to quickly create, transform, and modify contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems and archiving contours for patient follow-up and management

MIM 4.1 (SEASTAR) also | image processing and visualization tools:
x Multi-modality viewing and contouring of anatomical, functional, and multi-parametric images such as but not limited to CT, PET, PET/CT, MRI, Linac Cone Beam CT (CBCT) images and dose distributions
x Multiplanar reconstruction (MPR) thin/thick, minimum intensity projection (MIP), volume rendering technique (VRT)
x Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
x Creation of contours on any type of images without prior assignment of a planning CT
x Manual and semi-automatic | - it allows automatic segmentation on MR images which is not possible with ART-Plan v1.6.1 but covered by reference devices (MIM 4.1. and Contour ProtégéAl)

it can generate synthetic CT from MR images which is not possible with ART-Plan v1.6.1 but covered by reference devices (MRCATpelvis, MRCAT brain and Syngo.via RT Image Suite
it allows a cloud-based deployment which is not possible with ART-Plan v1.6.1 but covered by Contour ProtégéAl and Syngo.via RT Image Suite |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | | | | | |

9

interest structures).	registered directly but	aids in the	registration
These images, contours
and objects can
subsequently be
exported/distributed
within the system,
across computer
networks and/or to
radiation treatment
planning systems.
Supported modalities
include CT, PET-CT,
CBCT, 4D-CT and MR
images.	via the registration of
the CT of the PET
toward the target
image. The result of
the registration
operation can assist
the user in assessing
changes in image
data, either within or
between
examinations and
aims to help the user
obtain a better
understanding of the
combined information
that would otherwise
have to be visually
compared
disjointedly.	assessment of
PET/SPECT brain
scans. It provides
automated
quantitative and
statistical analysis
by automatically
registering
PET/SPECT brain
scans to a
standard template
and comparing
intensity values to
a reference
database or to
other PET/SPECT
scans on a voxel
by voxel basis,
within stereotactic
surface
projections or
standardized
regions of
interest.	using rigid and
deformable
registration
x Supports the
user in
comparing,
contouring, and
adapting
contours based
on datasets
acquired
with different
imaging
modalities and
at different time
points
x Supports the
user in
comparing
images and
contours of
different
patients
x Supports
multi-modality
image fusion
x Visualization
and contouring
of moving
tumors and
organs
x Management
of points of
interest
including but not
limited to the
isocenter
x Management
of simple
treatment plans
x Generation of
a synthetic CT
based on
multiple
ART-Plan supports
Al-based contouring on
CT and MR images and
offers semi-automatic
and manual tools for
segmentation.

To help the user assess
changes in image data
and to obtain combined
multi-modal image
information, ART-Plan
allows the registration
of anatomical and
functional images and
display of fused and
non-fused images to
facilitate the
comparison of patient
image data by the user.

With ART-Plan, users
are also able to
generate, visualize,
evaluate and modify
pseudo-CT from MRI
images. | ART-Plan provides a
number of tools such
as regions of
interests, which are
intended to be used
for the assessment of
regions of an image
to support a clinical
workflow. Examples
of such workflows
include, but are not
limited to, the
delineation of
anatomical regions of
interest on
3D-images of cancer
patients for whom
radiotherapy
treatment has been
planned.
ART-Plan supports
the loading and
saving of DICOM RT
objects and allows
the user to define,
import, display | Indications for
Use

MIM 4.1
(SEASTAR)
software is used
by trained medical
professionals as a
tool to aid in
evaluation and
information
management of
digital medical
images. The
medical image
modalities
include, but are
not limited to, CT,
MRI, CR, DX,
MG | | |
| | transform, store and
export such objects
including regions of
interest structures to
radiation therapy
planning systems.
ART-Plan allows the
user to transform
regions of interest
associated with a
particular imaging
dataset to another,
supporting Al-based
contouring on CT
images along with
semi-automatic and
manual tools for
segmentation. | US, SPECT, PET
and XA as
supported by
ACR/NEMA
DICOM 3.0. MIM
4.1
(SEASTAR)
assists in the
following
indications:

Receive,
transmit, store,
retrieve, display,
print, and process
medical images
and DICOM
objects.
Create, display
and print reports
from medical
images.
Registration,
fusion display,
and review of
medical images
for diagnosis,
treatment
evaluation, and
treatment
planning.
Evaluation of
cardiac left
ventricular
function and
perfusion,
including left
ventricular
end-diastolic
volume,
end-systolic
volume, and
ejection fraction.
Localization and
definition of
objects such as | pre-define MR
acquisitions | |
| | | | | |
| | | tumors and | | |
| | | normal tissues in | | |
| | | medical images. | | |
| | | * Creation, | | |
| | | | | |
| | | transformation, | | |
| | | and modification | | |
| | | of contours for | | |
| | | applications | | |
| | | including, but not | | |
| | | limited to, | | |
| | | | | |
| | | quantitative | | |
| | | analysis, aiding | | |
| | | adaptive therapy, | | |
| | | transferring | | |
| | | contours to | | |
| | | radiation therapy | | |
| | | | | |
| | | treatment | | |
| | | planning systems, | | |
| | | and | | |
| | | archiving contours | | |
| | | for patient | | |
| | | follow-up and | | |
| | | management. | | |
| | | * Quantitative and | | |
| | | | | |
| | | statistical analysis | | |
| | | of PET/SPECT | | |
| | | brain scans by | | |
| | | comparing to | | |
| | | other registered | | |
| | | PET/SPECT brain | | |
| | | scans | | |
| | | | | |
| | | | | |
| | | Lossy | | |
| | | compressed | | |
| | | mammographic | | |
| | | images and | | |
| | | digitized film | | |
| | | screen images | | |
| | | must not be | | |
| | | | | |
| | | reviewed for | | |
| | | primary image | | |
| | | interpretations. | | |
| | | lmages that are | | |
| | | printed to film | | |
| | | must be printed | | |
| | | using a | | |
| | | | | |
| | | FDA-approved
printer for the
diagnosis of
digital
mammography
images.
Mammographic
images must be
viewed on a
display
system that has
been cleared by
the FDA for the
diagnosis of
digital
mammography
images. The
software is not to
be used for
mammography
CAD | | |

10

11

12

System Information Comparison

	System Information
Property	Proposed
Device
ART-Plan
v1.10.0	Primary
Predicate
ART-Plan
v1.6.1	Reference
device
Contour
ProtégéAl	Reference
device
MIM 4.1	Reference
device
MRCAT Pelvis	Reference
device
MRCAT Brain	Reference
device
Syngo.via RT
Image Suite	Comment
Method of
Use	Standalone
software
application
accessed via
a compliant
browser
(Chrome or
Mozilla
Firefox) on a
personal
computer,
tablet or	Standalone
software
application
accessed via
a compliant
browser
(Chrome or
Mozilla
Firefox) on a
personal
computer,
tablet or	Standalone
software
application	Standalone
software
package	Provided as a
plug-in clinical
application to
Ingenia MR-RT. It
is compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT. It runs
parallel to image	Provided as a
plug-in clinical
application to
Ingenia MR-RT. It
is compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT. It
runs parallel to	syngo.via can be
used as a
standalone device
or together with a
variety of
syngo.via-based
software options,
which are medical
devices in their
own right.	The proposed device and
predicates (especially the
primary predicate) have
identical methods of use
	phone (In
case of
connection to
the platform
with a screen
of a phone or
a tablet, the
user must
choose the
option for the
desktop site of
his
communicatio
n device. The
platform is
optimally used
with 17 inches
and up
screen.
Facilitates
display and
visualization
of data by
user.	phone (In
case of
connection to
the platform
with a screen
of a phone or
a tablet, the
user must
choose the
option for the
desktop site
of his
communicatio
n device. The
platform is
optimally used
with 17 inches
and up
screen.
Facilitates
display and
visualization of
data by user.		acquisition on the
MR console,
embedded
post-processing
generates MRCAT
images using: •
Automated
segmentation and
tissue
classification •
Automated
assignment of
CT-based density
values	image acquisition
on the MR
console,
embedded
post-processing
generates MRCAT
images using: •
Automated
segmentation and
tissue
classification •
Automated
assignment of
CT-based density
values
Computer
Platform and
Operating
System	Full web
platform
Launch from
Google
Chrome or
Mozilla Firefox
Available on
server-based
application or
Cloud-based
deployment	Full web
platform
Launch from
Google
Chrome or
Mozilla Firefox	Server-based
application
supporting
Linux-based OS

and -
Local
deployment on
Windows or Mac
Cloud-based
deployment | Windows
2000/XP | As the density
information is
generated directly
on the MR
console, the
resulting data is
available at the
console for
immediate review. | As the density
information is
generated directly
on the MR
console, the
resulting data is
available at the
console for
immediate review. | This solution is
also available
cloud-based,
providing
scalability with
flexible use
models and cloud
deployment1 | The proposed device and
predicates are compatible with
identical operating systems. | |
| Data
Visualization
/ Graphical
Interface | Yes | Yes | Yes | Yes | Yes | Yes | Yes | The proposed device and all
the predicates have a data
visualisation and graphical
interface | |
| Synthetic CT | Generation of
CT density | N/A | N/A | N/A | Generation of CT
density image | Generation of CT
density image | Generation of CT- | The proposed device and
reference devices such as | |
| Supported
Modalities | image series
out of multiple
MR-image
series | Registration:
Static and
gated CT, MR,
PET (via the
registration of
the CT of said
PET), 4D-CT
and CBCT.

Segmentatio
n:
CT (injected
or not),
MR images,
DICOM
RTSTRUCT | Registration:
Static and
gated CT, MR,
PET (via the
registration of
the CT of said
PET)

Segmentatio
n:
CT (injected or
not), DICOM
RTSTRUCT | CT and MR | Medical image
modalities
include, but are
not limited to,
CT, MRI, CR,
DX, MG, US,
SPECT, PET and
XA as supported
by ACR/NEMA
DICOM 3.0. | series out of
multiple MR-image
series | series out of
multiple MR-image
series | density image
series out of
multiple MR-image
series | MRCAT pelvis, MRCAT brain
and Syngo.via RT Image Suite
have the same feature |
| | | | | | | MR images | MR images | 3D: CT, PET1,
PET/CT1, MRI1,
4D-CT and Linac
Cone Beam CT
(CBCT) image
support
Support for time
resolved CT and
MR1 images (e.g.
MR DCE,
Perfusion CT) | The proposed device is
compatible with the same
modalities as the primary
predicate on the registration
feature, which are CT, MR and
PET images in a DICOM
format. However, the proposed
device supports 2 additional
modalities:

CBCT (covered by
the reference device
Syngo.via RT Image
Suite)
4D-CT (covered by
the reference device
Syngo.via RT Image
Suite

For both devices, supported
images can be fixed (static) or
moving (gated).

The proposed device and the
primary predicate are both
compatible with CT images
(injected or not), DICOM and
RTSTRUCT on the
segmentation feature.

For MR images, the proposed
device and some reference
devices (such as MRCAT
pelvis and MRCAT brain) are
both compatible with MR
images on the segmentation
feature. |

13

1 Information obtained from their brochure.https://cont.com/39b415b7b6de2d907/a4c5e63e0880cd1/2053f68eadshs-syngo-via-timage-suite-brochure-2021.ptf

14

15

| | | | | | | | | Compared to reference
devices, the proposed device
claims less supported
modalities |
|---------------|---------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|-------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| Data Export | Distribution of
DICOM
compliant
Images into
other DICOM
compliant
systems. | Distribution of
DICOM
compliant
Images into
other DICOM
compliant
systems. | As supported by
ACR/NEMA
DICOM 3.0. | The system has
the ability to
send data to
DICOM-ready
devices for
image storage,
retrieval and
transmission. | MRCAT images
can be exported in
DICOM format
enabling the use
as primary images
in the treatment
planning systems | MRCAT images
can be exported in
DICOM format
enabling the use
as primary images
in the treatment
planning systems | DICOM, HL7 and
IHE-RO standard
compliance | The proposed device and
predicates (especially the
primary one) have identical
data export capabilities with
DICOM format. |
| Compatibility | Compatible
with data from
any DICOM
compliant
scanners for
the applicable
modalities. | Compatible
with data from
any DICOM
compliant
scanners for
the applicable
modalities. | supported by
ACR/NEMA
DICOM 3.0 | The software can
receive, transmit,
store, retrieve,
display, print,
and process
DICOM objects
and medical
image modalities
including, but not
limited to, CT,
MRI, CR, DX,
MG, US, SPECT,
PET and XA as
supported by
ACR/NEMA
DICOM 3.0. | MR console:
Compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT
After export,
compatible with
any DICOM
compliant
scanners. | MR console:
Compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT
After export,
compatible with
any DICOM
compliant
scanners. | Compatible with
DICOM
Automatic send to
TPS configuration | The proposed device and
predicates (especially the
primary one) have identical
compatibility (DICOM format) |

16

Technical Information Comparison

		Technical Information
Property	Proposed
Device
ART-Plan
v1.10.0	Primary
Predicate
ART-Plan
v1.6.1	Reference
device
Contour
ProtégéAl	Reference
device
MIM 4.1	Reference
device
MRCAT Pelvis	Reference
device
MRCAT Brain	Reference
device
Syngo.via RT
Image Suite	Comment
Delineation
Method	Al	Al	Al	Atlas	N/A	N/A	Deep learning
autocontouring
for organs at risk
(incl. lymph
nodes)2	The proposed device,
primary predicate and
most of the reference
devices share an Al
delineation method.
Image
registration	Multi-modal
and
mono-modal.
Rigid and
deformable
Automatic and
manual
initialization
(landmarks,
fusion box,
alignment).
Registration for
the purposes
of replanning/
recontouring
and Al-based
automatic
contouring.	Multi-modal and
mono-modal.
Rigid and
deformable
Automatic and
manual
initialization
(landmarks,
fusion box,
alignment).
Registration for
the purposes of
replanning/
recontouring
and Al-based
automatic
contouring.	N/A	Registration, fusion
display, and review
of medical images
for diagnosis,
treatment
evaluation, and
treatment planning.	N/A	N/A	Image Fusion
Rigid and
Deformable
Registration with
region-of interest
based
registration and
multiple
registrations per
image pair
Manual editing
of registrations
Save
registrations and
save deformed
images as
reformatted
dataset2
Contour warping
and display of
prior and new
structure set
Registration
Quality Check
with spyglass,
deformation
vector map	Both the predicate device
and ART-Plan offer
mono-modal (CT-CT) and
multi-modal (CT/MR,
CT/PET) rigid and
deformable registration.
However, the proposed
device supports 2
additional modalities:

CBCT (covered
by the reference
deviceSyngo.via
RT Image Suite)
4D-CT (covered
by the reference
deviceSyngo.via
RT Image Suite
Both devices offer an
automatic solution for
registration and
semi-automatic registration
by including manual
initialization tools in
addition to automatic
initialization. | | |
| Segmentation
Features | Automatically
delineates
OARs and
healthy lymph
nodes
Deep learning
algorithm.
Automatic
segmentation
includes the
following
localizations:

head and
neck (on CT
images)
thorax/breast
(for
male/female
and on CT
images)
abdomen (on
CT images and
MR images)
pelvis
male(on CT
images and
MR images)
pelvis female
(on CT
images)
brain (on CT
images and
MR images) | Automatically
delineates
OARs and
healthy lymph
nodes (on any
CT images)
Deep learning
algorithm.
Automatic
segmentation
includes the
following
localizations:
head and
neck
thorax/breast
(for
male/female)
abdomen
pelvis (for
male only)
brain. | Creation of
contours using
machine-learning
algorithms for
applications
including, but not
limited to,
quantitative
analysis, aiding
adaptive therapy,
transferring
contours to
radiation therapy
treatment planning
systems, and
archiving contours
for patient follow-up
and management.
Segmenting
anatomical
structures across a
variety of CT
anatomical
locations.
And segmenting
normal structures
of the prostate,
seminal vesicles,
and urethra within
T2-weighted MR
images. | The software
automatically
generates contours
using a deformable
registration
technique which
registers
pre-contoured
patients to target
patients.
Registrations are
either
between a serial
pair of intra-patient
volumes or
between a
pre-existing atlas of
contoured patients
and a patient
volume. This
process facilitates
contour creation or
re-contouring for
adaptive therapy. | N/A | N/A | magnitude color
map | Reference device such as
Syngo.via RT Image Suite
offers the same options as
the proposed device: rigid
and deformable
registration. | | |
| | | | | | | | Multimodality
contouring
Freehand 2D,
3D image-based
Smart Freehand
segmentation, s
Contour on any
arbitrary plane
including oblique
planes
deep learning
autocontouring
for organs at risk
(inclusive LNs)
One-click
adaptive
contouring
User
configurable
Organ
Templates
Multiple
structure set
support (1 per
image series)
Molecular
imaging data
such as PET,
threshold-based
and skin, gray
value-based
segmentation1
"CT-free"
contouring:
native PET or
MR contouring | The proposed device and
primary predicate are
capable of automatically
contouring the
organ-at-risk (OAR) and
healthy lymph nodes using
Al (deep learning)
algorithm.
There is a difference in
intended anatomies for CT
images as the proposed
device also includes pelvis
female.
For MR images, all
anatomies included in the
proposed device are also
included in the primary
predicate. | | |

² https://www.siemens-healthineers.com/radiotherapy/software-solutions/syngovia-rt-image-suite (last checked on Feb, 15th 2022)

17

18

| | | | | | | | Parallel
contouring:
contouring
performed on
any image is
reflected on all
other images
Visualization of
previously drawn
structures on the
current image
series
Contour copy
and warping
between image
series2 | |
|--------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|-----|-----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|
| View
Manipulation
and
Volume
Rendering | Window and
level, pan,
zoom,
cross-hairs,
slice
navigation.
Maximum,
average and
minimum
intensity
projection
(MIP, AVG,
MinIP),
color
rendering,
multi-planar
reconstruction
(MPR), fused
views,
gallery views. | Window and
level, pan,
zoom,
cross-hairs,
slice
navigation.
Maximum,
average and
minimum
intensity
projection (MIP,
AVG, MinIP),
color rendering,
multi-planar
reconstruction
(MPR), fused
views,
gallery views. | Not stated | Not stated | N/A | N/A | Organ algebra
(union,
intersection,
exclusion)
Symmetric and
asymmetric
structure growth
or contraction
Smart 2D/3D
Nudge, brush
Pan, scale,
rotate contour
Geometrical and
smart
image-based
contour
interpolation
Multi-modality
Image
Manipulation
Multiplanar
reconstruction
(MPR) thin/thick,
minimum
intensity
projection (MIP),
volume | The proposed device has
the same tools as the
primary predicate. |
| | | | | | | | rendering
technique
(VRT)2 | |
| Regions and
Volumes
of Interest
(ROI) | AI Based
autocontouring,
Registration
based contour
projection
(re-contouring),
Manual ROI
manipulation
and
transformation
(margins,
booleans
operators,
interpolation). | AI Based
autocontouring,
Registration
based contour
projection
(re-contouring),
Manual ROI
manipulation
and
transformation
(margins,
booleans
operators,
interpolation). | AI Based
contouring, tools to
quickly create,
transform, and
modify contours. | Atlas based
contouring, tools to
quickly create,
transform, and
modify contours. | N/A | N/A | syngo.via RT
Image Suite
provides
dedicated tools,
which help the
medical
professional in
contouring
and evaluating
volumes of
interest.
Freehand and
semi-automatic
contouring of
regions-of-intere
st on any
orientation
including
oblique2 | Both the proposed device
and the primary predicate
allow AI automatic
contouring and manual
contouring |
| Region/volum
e of
interest
measurement
s and
size
measurement
s | Intensity,
Hounsfield
units and SUV
measurements
Size
measurements
include 2D and
3D
measurements
(number of
slices, volume
of a structure,
static ruler) | Intensity,
Hounsfield units
and SUV
measurements
Size
measurements
include 2D and
3D
measurements
(number of
slices, volume
of a structure,
static ruler) | N/A | Quantitative
analysis tools. | N/A | N/A | N/A | The proposed device
offers the same kind of
region/volume of interest
measurements and size
measurements as the
primary predicate |

19

20

Device Description:

The ART-Plan application is comprised of two key modules: SmartFuse and Annotate, allowing the user to display and visualize 3D multi-modal medical image data. The user may process, render, review, store, display and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities cover static and gated CT (computerized tomography including CBCT and 4D-CT), PET (positron emission tomography) and MR (magnetic resonance).

Compared to ART-Plan v1.6.1 (primary predicate), the following additional features have been added to ART-Plan v1.10.0:

· an improved version of the existing automatic segmentation tool
· automatic segmentation on more anatomies and organ-at-risk
image registration on 4D-CT and CBCT images .
automatic segmentation on MR images .
· generate synthetic CT from MR images
a cloud-based deployment

The ART-Plan technical functionalities claimed by TheraPanacea are the following:

. Proposing automatic solutions to the user, such as an automatic delineation, automatic multimodal image fusion, etc. towards improving standardization of processes/ performance / reducing user tedious / time consuming involvement.
. Offering to the user a set of tools to assist semi-automatic delineation, semi-automatic registration towards modifying/editing manually automatically generated structures and adding/removing new/undesired structures or imposing user-provided correspondences constraints on the fusion of multimodal images.
. Presenting to the user a set of visualization methods of the delineated structures, and registration fusion maps.
. Saving the delineated structures / fusion results for use in the dosimetry process.
. Enabling rigid and deformable registration of patients images sets to combine information contained in different or same modalities.
Allowing the users to generate, visualize, evaluate and modify pseudo-CT from MRI images.

ART-Plan offers deep-learning based automatic segmentation for the following localizations:

head and neck (on CT images) .
. thorax/breast (for male/female and on CT images)
abdomen (on CT images and MR images) ●
. pelvis male(on CT images and MR images)
. pelvis female (on CT images)
brain (on CT images and MR images)

ART-Plan offers deep-learning based synthetic CT-generation from MR images for the following localizations:

pelvis male .
brain

21

Information about your training dataset:

Summary test statistics or other test results including acceptance criteria or ● other information supporting the appropriateness of the characterised performance
Acceptance criteria for performance of ART-Plan modules were established using performance ranges extracted from benchmark devices and alternative technologies in the literature. For an auto segmentation model to be judged acceptable, every organ included in the model must pass at least one acceptance criterion with success across the different testings it has been submitted to. These criteria are as follows:
A. The Dice Similarity Coefficient (DSC) is equal to or superior to the acceptance criteria set by the AAPM: DSC (mean)≥ 0.8. Or
B. The Dice Similarity Coefficient (DSC) is equal to or superior to inter-expert variability: DSC (mean)≥ 0.54 or DSC (mean)≥ mean (DSC inter-expert) + 5% Or
C. The clinicians' s qualitative evaluation of the auto-segmentation is considered acceptable for clinical use without modifications (A) or with minor modifications / corrections (B) with a A+B % above or equal to 85% considering the following scale:

A: the contour is acceptable for a clinical use without any modification B: the contour would be acceptable for clinical use after minor modifications/corrections

C: the contour requires major modifications (e.g. it would be faster for the expert to manually delineate the structure)"

For the synthetic-CT generation tool, the acceptance criteria are as follows:

A. A median 2%/2mm gamma passing criteria of ≥95%
B. A median 3%/3mm gamma passing criteria of ≥99.0%
C. A mean dose deviation (pseudo-CT compared to standard CT) of ≤2% in ≥88% of patients

Total number of individual patients images in the reported auto segmentation ● tools and independence of test data and training data

Our training, validation and test cohorts are built from real-world retrospective data which were initially used for treatment of cancer patients. For the structures of a given anatomy for a qiven modality (MR or CT), two non-overlapping data sets were separated: the test patients (number selected based on thorough literature review and statistical power) and the train data. We make sure that those sets are non-overlapping and further split the train cases into train and validation sets and ensure enough train cases for the machine learning models to converge and achieve good performances of the validation set.

22

	Sample Size	%
Training	299142	0.8
Validation	75018	0.2
Total	374160	1

Total number of cases and samples images in the reported auto segmentation . results

The total number of patients used for training (8736) is lower than the number of samples (374160). This is linked to the fact that one patient can be associated with more images (e.g. CT. MR) and that each image (anatomy) has the delineation of several structures (OARs and lymph nodes) which increases the number of samples used for training and validation.

Demographic distribution including gender, age and ethnicity ●

All data used for training of the models have been pseudo-anonymised by the centers providing data before transfer. Around 80% of the data used for training contain information on gender and age of the patients. In terms of gender, around 44% and 56% of our data (that contains this information) are from female and male patients, respectively. In comparison, in 2020 according to the Global Cancer Observatory, 48% and 52% of the cancer patients were female and male, respectively.

In terms of age, our data follows the same trend observed and reported in the US (SEER NIH), UK (Cancer Research UK) and worldwide (Global Cancer Observatory) for cancer incidence according to age, with more than 95% of the data coming from patients between 20 and 85 vears old. Our data has a slight overrepresentation (8% points) for the ages between 54 and 60 years old, at the cost of a slight underrepresentation of patients in the age range between 20-34 (1.5% points) and above 85 (6.5% points) years old. In addition, following the general global (incl US) trend, our data also depicts a steep rise in the incidence rate from in the age group of 55-64 years old, with a median age of 63 years old (as compared to 66 years old in the US).

Although this information is not exhaustive, this analysis shows that the demographic distribution in terms of age and gender of the data used for training and validation of the models are well aligned with the incidence cancer statistics found for instance in US, UK and globally. This comes from the fact that real clinical data provided by medical facilities without any selection criteria (i.e. no discrimination or selection has been applied to the cases retrieved), leading to the demographic distribution including gender and age across the data is representative of the distribution in the clinic and thus of the cancer patient population in general.

An exception is noted for following models that are gender-dependent:

100% of pelvis images for male pelvis model for automatic annotation are male patients
100% of pelvis images for female pelvis model for automatic annotation are female patients
100% of breast images for the breast automatic annotation are female patients
100 % of pelvis images for automatic synthetic-CT generation are male patients

No pseudo-anonymized data included any information on the ethnicity.

23

In addition, automatic delineation of the device demonstrated equivalent performances between non-US and US population.

On the "truthing" and data collection process .

The contouring quidelines followed to produce the contours were confirmed with the centers which provided the data. Our truthing process includes a mix of data created by different delineators (clinical experts) and assessment of intervariability, ground truth contours provided by the centers and validated by a second expert of the center, and qualitative evaluation and validation of the contours. This process ensures that the data used for training and testing can be considered representative of the delineation practice across centers and following international guidelines.

On clinical subgroups, confounders and equipment details .

In general, confounding factors affecting health status present in the dataset could be related to patient clinical variables such as age, gender, ethnicity, economical and educational levels. As shown in "Demographic distribution including gender, age and ethnicity", our data is representative of the demographic cancer distribution in terms of gender and age. In addition, our models when appropriate (i.e. for gender independent anatomies) are shared across gender removing any further bias and augmenting substantially training cohorts.

Variables like ethnicity, economical and educational status that could be associated with obesity are further confounding factors that could impact global patient's anatomy and introduce bias in the performance of the obtained solution. To address this aspect, we have adopted a strategy that projects a patient's specific anatomy to common, multiple, different in size, full-body female and male patient templates, allowing a direct harmonization of data resulting in potential removal of bias of anatomical diversity across ethnic, economical and educational groups. Please note that this information (ethnic group, educational/economical level, etc.) is often not available in the pseudo-anonymised data and therefore performing statistical tests and increasing the number of operations allowing to separate correlations from causality is often unattainable.

Reqarding variables associated with treatment therapeutic and treatment implementation strategies; we can imaging devices and treatment devices being potential confounding factors as differences exist among CT and MR scanners manufacturers that could potentially introduce bias. We have addressed this concern through a statistical analysis of the different imaging vendors in EU & USA towards the creation of a data training, validation and testing cohort that globally appropriately represents the market share of the different vendors allowing generalization and removing hardware specific bias. In terms of treatment implementation, it should be noted that different guidelines exist and depending on the treatment device different therapeutic constraints and quidelines are applied. This is reflected in our database since different strategies and constraints are used depending on the choice of treatment (e.g external radiotherapy vs stereotactic treatment). Our solution, due to its concept of removing bias through projection to patient template anatomies as well as due to the component-based approach that is able to aggregate training data across imaging and treatment vendors, is able to address the maximum set of constraints. Therefore, we do not introduce any bias on the type of treatment that will be delivered (supporting any type of clinically conventionally adopted treatment from manufactures such as Varian, Elekta, Accuray, GE, Siemens, ViewRay & Zap

24

Surgical), providing direct means for customization of the constraints to be met at the clinical expert level and offering a representative coverage of all vendors in radiation oncology world-wide.

An exception is noted for following models that are vendor-, machine- or sequence-dependent:

MR annotation tool for pelvis and abdominal regions were trained on data from a 0.35T MR machine provided by ViewRay
synthetic-CT generation tool for pelvis was trained on data from a 0.35T MR machine provided by ViewRay
synthetic-CT generation tool and annotation tool for MR pelvis was trained on data from 1.5T Philips (Elekta) for T2 sequences, and might not work on T1-weighted images

Technological Characteristics:

A comparative review of the ART-Plan with the predicate device found that the technology, mode of operation, and general principles for treatment with this device were substantially equivalent as the predicate device.

Non-Clinical Tests (Performance/Physical Data):

The ART-Plan was evaluated for its safety and effectiveness based on the following testing:

Test Name	Test Description/Results	Results
Usability Report
(V1.10.0)	This document is intended to document the
usability test results for the ART-Plan v1.10.0 for
compliance with IEC 62366-1:2015+AMD1:2020 -
Medical devices - Application of usability
engineering to medical devices.	Passed
Usability file - ART-USR-09
(V1.10.0)	The ART-Plan was assessed with regards to
usability for compliance with each section of IEC
62366	Passed
Usability - Testers qualification
(V1.10.0)	This table shows that European medical physicists
who have participated in the evaluation have at
least an equivalent expertise level compared to a
junior US medical physicist (MP), and
responsibilities in the radiotherapy clinical workflow
are equivalent	N/A
Literature Review and
Performance Criteria
Extraction Report for ART-Plan
(V1.9.0 and V1.10.0)	A literature review is performed to establish
acceptance criteria for performance of ART-Plan
modules using performance ranges observed from
benchmark devices and alternative technologies in
the literature. All measures of performance that
were established in this document were supported
by clinical evidence. It was also demonstrated,
from the clinical data, that ART-Plan has a clear	N/A
	clinical relevance in accordance with the clinical
state of the art.
ART-Plan performance testing

Overview
(V1.6.1-V1.10.0) | The document summarises all performance tests
that have been performed since the last FDA
cleared version (1.6.1). It also shows which criteria
have been met in each test for all modules of
ART-Plan. It demonstrates that all modules of
ART-Plan pass at least one performance
acceptance criterion and hence are clinically
acceptable for release. | Passed |
| Study Protocol and Report
Annotate Performances
Summary (V1.9.0) | The testing demonstrated that Annotate provides
acceptable contours for the concerned structures
on an image of a patient. | Passed |
| Abdo MRI auto-segmentation
performances according to
AAPM requirements (V1.8.0) | Mean DSC of each organ was compared with the
tolerance threshold of 0.8. After comparing the
contours of 3 different experts on the same patient
the mean DSC was calculated, compared with the
auto-segmentation and was observed to be in
every case superior. It is concluded that the
auto-segmentation algorithm provides clinical
acceptable contours. | Passed |
| Testing protocol/report - Brain
MRI autosegmentation
performances according to
AAPM requirements | In this test, some organs did not meet the
acceptance criteria. However, the value of 0.80 is
indicated by the AAPM as the "uncertainty of
contouring of the structure" which in fact can be
significantly below 0.80 depending on the organ.
Thus, we also decided to evaluate in parallel the
models with a qualitative evaluation of our
predictions (see additional qualitative test below). | Passed |
| Qualitative validation of
autoseqmentation
performances - Brain (V1.8.0) | All organs except left and right cochlea passed at
least one of the acceptance criteria demonstrating
that the Annotate module provides acceptable
contours on MR brain structures. The MR brain
model has been further improved, subjected to
further testing, and, after providing acceptable
contours for all structures (incl. the cochlea),
released in v1.10.0. (see Study Protocol and
Report- Autosegmentation performances against
inter-expert variability - Brain MR (V1.10.0)). | Passed |
| Qualitative validation of
auto-segmentation
performances - Gyneco
(V1.8.0) | The testing demonstrates that Annotate provides
acceptable contours for a specific list of
gynecological structures on a Female pelvis CT
image. Three testing methods are used: DICE
calculation Inter-expert DICE calculation
calculation,
and | Passed |
| | qualitative Indicator. All structures passed at least one of the acceptance criteria and were released. | |
| Pelvis MRI auto-segmentation tool performances according to AAPM requirements (V1.8.0) | The testing demonstrates that the auto-segmentation algorithm for Pelvis MRIs provides acceptable contours for the concerned structures on an image of a patient. All organs met at least one of the acceptance criteria and therefore were considered acceptable. | Passed |
| Qualitative & Quantitative validation of fusion performances (V1.9.0) | The study was developed to cover the major clinical use cases in which fusions are used in the radiotherapy workflow and split into as many sub-studies as clinical use cases of fusion in radiotherapy workflow. The results show that both types of fusion algorithms (Rigid & Deformable) in SmartFuse pass the performed tests, and provide valid results for further clinical use in radiotherapy. | Passed |
| Study Protocol and Report for qualitative validation of fusion performances for tCT_SCT_injected/PET modality (V1.9.0) | The study evaluated the quality of the rigid and the deformable fusion algorithms of the SmartFuse module for the following cases:
CT injected image fuse towards CT image
CT-PET image fuse towards CT image.
Both types of fusion algorithms, rigid and deformable, provided clinically acceptable results for the desired clinical uses. | Passed |
| Study Protocol & Report for qualitative validation of ITV calculation performances for 4D_CT modality (V1.9.0) | The testing evaluated the quality of ITV calculation algorithm of the Annotate module in the case of 4D-CT examinations. Acceptable results were reached for the evaluation of contours propagation. | Passed |
| Study Protocol and Report for validation of fusion performances for tCT_SMR modality (V1.9.0) | This testing evaluated the performances of the SmartFuse module for the clinical case of fusion of an MRI towards a planning CT to aid in the delineation. Acceptable results were reached for this evaluation. | Passed |
| Study Protocol and Report for qualitative validation of fusion performances for tMR_SCT modality (V1.9.0) | This study evaluated the performances of the SmartFuse module for fusion of CTs towards planning MRIs for the purpose of electron density transfer. Acceptable results were reached for this evaluation. | Passed |
| Study Protocol and Report for qualitative & quantitative validation of fusion performances for tMR_SMR modality (V1.9.0) | This study evaluated the performances of the SmartFuse module on the clinical case of using fusion for MRI replannification. Favorable results to the established performance criteria for rigid and deformable registrations, and for all organs, were obtained. | Passed |
| Protocol for qualitative & quantitative validation of fusion performances for tCT_SCT_replanning modality (V1.9.0) | This study evaluated the quality of the rigid and the deformable fusion algorithms of the SmartFuse module for replanification of CT-based treatments. Acceptable results were reached for this evaluation. | Passed |
| Pilot study for sample size
estimation - literature review
(V1.9.0) | The literature review was performed to estimate the
appropriate sample size of the testing data set
towards demonstrating the performance of the
image registration, segmentation and pseudo-CT
generation solutions on the basis of the most
recent and most relevant scientific literature. | N/A |
| Autoseg 2D regression test for
integration in ART-Plan V1.9.0 | The objective of the test was to demonstrate
equivalence between the version (V1.9.0) and
previous versions of Annotate (V.1.8 and v1.6.1).
All organs passed at least one of the defined
criteria, and hence were accepted for release in
v1.9. Given that equivalence TheraPanacea
considers all tests (especially the qualitative ones)
performed on previous versions of the software to
be still relevant. | Passed |
| Study Protocol & Report
(SPR): External contour
non-regression protocol for
integration in ART-Plan
V1.10.0 | This test demonstrates equivalence between the
version V.1.10 and V.1.9 of the external contours
and show that the new added anatomies in the
module Annotate provides clinically acceptable
contours. The external contour for all anatomies
passed the defined criteria, and hence were
accepted for release in the v1.10. | Passed |
| Testing Protocol/Report -
Autoseg CT, MR
non-regression test for
integration in ART-Plan
V1.10.0 | This test demonstrates equivalence between the
version v.1.10 and v.1.9 of the auto-segmentation
models for all structures and shows that the
updated models provide clinically acceptable
contours. All organs passed the defined criteria,
and were hence accepted for release in the v1.10. | Passed |
| Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for Thorax | This test demonstrates that Annotate provides
acceptable contours for structures of the thorax
region: thoracic aorta and bronchial trees. This
qualitative test was performed as an addition to
Section 18.21 to ensure the contours are clinically
acceptable. | Passed |
| Study Protocol and Report-
Autosegmentation
performances against
inter-expert variability - Brain
MR (V1.10.0) | This test demonstrates that the Annotate provides
clinically acceptable (compared to inter-expert
variability) for all MR-T1 Brain structures. Existing
structures present in the previous version (v.1.8 -
see Section 18.8) were also re-evaluated since a
complete retraining of the model was done. | Passed |
| Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for Pelvis Truefisp model | This test demonstrates that the module Annotate
acceptable contours for 9 organs
provides
evaluated on MR Truefisp images of patients. All
organs have passed the acceptance criterion of
reaching a percentage of at least 85% of A or B
(qualitative evaluation) and hence can be released
in v.1.10.0. | Passed |
| Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for H&N Lymph nodes | This test demonstrates that Annotate provides
acceptable contours for following cervical lymph
nodes levels: la, lb right, VIIa left, VIIb right, II left,
III right, V left, IVb right, IVb left. All cervical lymph
nodes having reached a percentage of at least | Passed |
| | 85% of A or B, the performance of auto segmentation is demonstrated, and hence all structures were included in V1.10. | |
| Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0 | This test demonstrates that Annotate provides clinically acceptable contours, following qualitative measures, for the new version v.1.10. It serves as an additional evaluation to Section 18.21, and was done on a set of organs of all anatomies for both the CT and MR models. The benchmarking was done not only against the qualitative evaluation but also against the manual contours and a previously validated version of the models. All contours can be considered as acceptable as at least one criterion was met for each of the included structures. | Passed |
| Study Protocol and Report
Annotate Performances
Summary (V1.10.0) | The purpose of this document is to describe all the testing protocols and testing results for validating the performance of the Annotate module.

Since all organs added in v.1.10.0 of Annotate have passed at least one test and met at least one acceptance criteria, all organs have been released. | Passed |
| Testing: pseudo-CT clinical
performance and comparison
to predicates (pelvis)
(V1.10.0) | The evaluation demonstrated the non-inferiority of using Annotate's pseudo-CT for treatment planning in terms of dosimetric measures as compared to CT-based treatment planning. Our pseudo-CT for pelvis has shown to produce results that meet the acceptance criteria derived from clinical practice and literature review as well as to perform at least as good as two FDA cleared devices for pseudo-CT generation. | Passed |
| Testing: pseudo-CT clinical
performance and comparison
to predicates (brain)
(V1.10.0) | The evaluation demonstrated the non-inferiority of using Annotate's pseudo-CT for treatment planning in terms of dosimetric measures as compared to CT-based treatment planning. Our pseudo-CT for pelvis has shown to produce results that meet the acceptance criteria derived from clinical practice and literature review as well as to perform at least as good as two FDA cleared devices for pseudo-CT generation. | Passed |
| Study Protocol & Report
(SPR): Testing:
Autosegmentation
performances against
predicates
(V1.10.0) | This evaluation demonstrated the non-inferiority of using ART-Plan v1.10.0 for annotation of organs as compared to other devices which have been cleared for use in the US. In addition, ART-Plan v1.10.0 offers almost 3 times (2.72) more organs than MIM/ContourProtege AI, which represents an additional benefit to the users as compared to other devices. | Passed |
| Study Protocol & Report
(SPR): Autosegmentation | In this test, Annotate demonstrated equivalent performances between non-US and US population | Passed |
| performances on Thorax US
data
(V1.10.0) | for the Thorax localisation. Considering that this is
a worst case scenario of morphological
deformation due to factors such as age, gender or
weight in abdominal region, we claim that
considering any localisation included in the
intended use of ART-Plan, any autosegmentation
result demonstrated on a non-US population can
be generalized to a US population. Nonetheless,
TheraPanacea has performed an additional
evaluation on pediatric US-data covering all other
localisations included in the intended use of the
device. | |
| Clinical evaluation of automatic
segmentation on Pediatric
images
(V1.10.0) | Considering the fact that all the structures have
reached a percentage of 90% (>=85%) of A or B
for the MR brain model, and that 11/15 structures
passed with success for the CT model,
Therapanacea claims that the performance of auto
segmentation on said organs has been
demonstrated for the studied population.
These results highlight the high generalizability of
the commercial tool, initially made for adults, to
pediatric cases and its clinical implementation
feasibility. Note that this study served to
demonstrate that ART-Plan's Annotate, trained on
European data, can be generalised to the US
population given that it would be clinically
acceptable even for pediatric cases where a more
prominent change in size is expected than the one
between two adults from different countries. This
does not mean that TheraPanacea is claiming that
ART-Plan should be used for pediatric patients. | Passed |
| System Verification and
Validation Testing | The system verification and validation testing was
performed to verify the software of the ART-Plan. | Passed |

25

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27

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29

Software Verification and Validation Testing

Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."

The software for this device was considered as a "major" level of concern, since a failure or latent design flaw could directly result in death or serious injury to the patient or a failure or provide diagnostic information that directly drives a decision regarding treatment or therapy, such that if misapplied it could result in serious injury or death.

Animal Studies

No animal studies were conducted as part of submission to prove substantial equivalence.

Clinical Studies

Page 27 of 28

30

No clinical studies were conducted as part of submission to prove substantial equivalence.

Safety and Effectiveness/Conclusion:

Based on the information presented in these 510(k) premarket notifications the TheraPanacea ART-Plan is considered substantially equivalent. The TheraPanacea ART-Plan is as safe and effective as the currently marketed predicate devices.

Based on testing and comparison with the predicate devices, TheraPanacea ART-Plan indicated no adverse indications or results. It is our determination that the TheraPanacea ART-Plan performs within its design specifications and is substantially equivalent to the predicate device.