Predicate Devices

Reference Devices

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The document explicitly mentions "machine learning models" and "AI-powered decision making process" in the description of the training data and performance studies.

Therapeutic

No.
Explanation: The device is a software application designed to assist medical professionals in the radiotherapy planning process by processing, visualizing, and analyzing medical image data, and helping to determine the need for replanning. It does not directly provide therapy or affect the body's structure or function; it is a tool for therapy planning.

Diagnostic

No

The device is described as assisting in radiotherapy treatment planning by processing and visualizing medical image data, proposing automatic solutions for delineation and image fusion, and generating synthetic CTs. While it deals with medical images, its functions are geared towards planning and adapting radiotherapy, including assessing the need for replanning, rather than diagnosing a patient's medical condition. It supports treatment, not diagnosis.

SaMD (Software as a Medical Device)

Yes

The device description explicitly states "The ART-Plan application consists of three key modules" and describes software functionalities like displaying, processing, rendering, reviewing, storing, and distributing medical image data. While it interacts with medical imaging hardware (DICOM datasets), the device itself is the software application.

IVD (In Vitro Diagnostic)

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

Here's why:

IVD Definition: In Vitro Diagnostics are medical devices used to perform tests on samples taken from the human body, such as blood, urine, or tissue, to provide information about a person's health. They are used outside the body (in vitro).
ART-Plan's Function: ART-Plan is a software application used for radiotherapy treatment planning. It processes and visualizes medical imaging data (CT, MR, etc.) to assist medical professionals in planning radiation therapy for cancer patients. This process involves analyzing images of the patient's anatomy and tumors, not analyzing samples taken from the body.
Intended Use: The intended use clearly states it's for cancer patients for whom radiotherapy treatment has been prescribed and involves processing imaging data.
Device Description: The description focuses on image processing, visualization, registration, and dose calculation, all of which are related to treatment planning based on imaging, not laboratory testing of biological samples.

Therefore, ART-Plan falls under the category of medical imaging software or treatment 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 PCCP for this specific device.

Overview

Intended Use / Indications for Use

Intended Use: ART-Plan is a software intended to be used by trained clinicians who are familiar with radiation therapy, such as medical physicists, medical dosimetrists and radiation oncologists. The software consists of different applications, each used for specific purposes at a different phase of radiation treatment planning.

ART-Plan offers the following tools to aid in the workflow of radiotherapy treatment:

Multi-modal visualization and rigid- and deformable registration of anatomical and . functional images such as CT, MR, PET-CT, 4D-CT, CBCT and synthetic-CT generated from CBCT
Display of fused and non-fused images to facilitate the comparison and delineation of image data by the user
Manual generation, 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 ● on medical practices, on medical images such as CT and MR images
Generation of synthetic-CT from MR images for supported anatomies
Generation of synthetic-CT from CBCT images for supported anatomies ●
Dose computation on CT and/or synthetic-CT images for external beam irradiation ● with photon beams
Assisted CBCT-based off-line adaptation decision-making for supported anatomies

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

Indications for Use: ART-Plan's indicated target population is cancer patients for whom radiotherapy treatment has been prescribed. In this population, any patient for whom relevant modality imaging data is available.

ART-Plan is not intended for patients less than 18 years of age.

The indicated users are trained medical professionals includinq, but not limited to, radiotherapists, radiation oncologists, medical physicists, dosimetrists and medical professionals involved in the radiation therapy process.

The indicated use environments include, but are not limited to, hospitals, clinics and any health facility involved in radiation therapy.

Product codes

MUJ, QKB, LLZ

Device Description

The ART-Plan application consists of three key modules: SmartFuse,Annotate and AdaptBox, allowing the user to display and visualise 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.

Compared to Ethos Treatment, 2.1; Ethos Treatment Planning, 1.1 (primary predicate), the following additional feature has been added to ART-Plan v2.1.0:

generation of synthetic CT from MR images. This does not represent an additional . claim as the technological characteristics are the same and it does not raise different questions of safety and effectiveness. Also, this feature is already covered by reference and previous version of the device ART-Plan v1.10.1.

Generation of Synthetic CT	ART-Plan V2.1.0 (Proposed device)	Ethos Treatment Management, 2.1; Ethos Treatment Planning, 1.1 (primary predicate)	ART-Plan v1.10.1 (reference device and previous version of the proposed device)
from MR images	✓		✓
from CBCT images	✓	✓

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 addina/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 and CBCT images.
Allowing the users to generate, visualize and analyze dose on images of CT modality (only within the AdatpBox workflow)
Presenting to the user metrics to define if there is a need for replanning or not.

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, on synthetic-CT from CBCT and on 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

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

· pelvis male

Mentions image processing

Yes. "Radiological image processing software for radiation therapy" is listed as a common name for a reference device, and "LLZ (System, Image Processing, Radiological)" is a secondary product code.

Mentions AI, DNN, or ML

Yes. "AI-based automatic contouring", "Deep learning algorithm.", "AI" in delineation method, "Al-powered decision making process for RT re-planning".

Input Imaging Modality

CT, MR, PET-CT, 4D-CT, CBCT, synthetic-CT generated from CBCT.

Anatomical Site

head and neck, thorax/breast, abdomen, pelvis male, pelvis female, brain.

Indicated Patient Age Range

Not intended for patients less than 18 years of age.

Intended User / Care Setting

Intended User: trained medical professionals including, but not limited to, radiotherapists, radiation oncologists, medical physicists, dosimetrists and medical professionals involved in the radiation therapy process.
Care Setting: hospitals, clinics and any health facility involved in radiation therapy.

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

Training Set Sample Size:

Auto-segmentation tool: 246226 samples
Synthetic-CT from MR images: 6195 samples
Synthetic-CT from CBCT images: 1467 samples

The number of patients used for training of auto-segmentation (8950) is lower than the number of samples (246226). 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. The same rationale for the generation of synthetic-CT from CBCT or MR images.

Data Source: Real-world retrospective data initially used for treatment of cancer patients. Data was pseudo-anonymised by the centers providing data before transfer.

Demographic Distribution:

Gender: around 44% female, 56% male (of data containing this information)
Age: more than 95% of data from patients between 20 and 85 years old. Slight overrepresentation (8% points) for ages between 54 and 64, slight underrepresentation for 20-34 (1.5% points) and above 85 (2.1% points). Median age 64.
Ethnicity: Not able to assess data distribution on ethnicity as information not available in pseudo-anonymized data.
Anatomies: 16.4% brain, 28.1% head and neck, 24.6% thorax, 4.4% abdominal, 26.4% pelvis.
Gender-dependent models: 100% of pelvis images for male pelvis model are male patients, 100% of pelvis images for female pelvis model are female patients, 100% of breast images are female patients, 100% of pelvis images for automatic synthetic-CT generation from CBCT and MR are male patients.

Truthing and Data Collection Protocol (Autosegmentation):
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 is following international guidelines.

Truthing and Data Collection Protocol (Synthetic-CT from MR or CBCT):
The clinical evaluation as part of the "truthing-process" guidelines followed to produce and validate the synthetic-CTs were extracted from the literature and confirmed with the centers which provided the data and helped in the performance evaluation. Our truthing process includes imaging metrics based comparison between synthetic-CTs and real planning CTs for the same patients. The real planning CTs come from a mix of machines and centers to avoid any bias. In addition, the evaluation includes a non-inferiority assessment of the capability of the synthetic-CT to be used for dose generation purposes as compared to a planning CT. This process ensures that the data used for training and testing can be considered representative of the clinical practice across centers and following the processes outlined and reviewed through the literature review.

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

Test Set Sample Size: The test set is part of the non-overlapping data sets separated from the real-world retrospective data, with the number of patients selected based on thorough literature review and statistical power. The validation set size is between 10% and 25% of the training data.

Data Source: Real-world retrospective data which were initially used for treatment of cancer patients, pseudo-anonymised by the centers providing data before transfer.

Annotation Protocol: Not explicitly stated for the test set, but it is implied to follow the same "truthing" process as described for the training set where data is created by different delineators (clinical experts), with assessment of intervariability, ground truth contours validated by a second expert, and qualitative evaluation and validation of contours.

Summary of Performance Studies

Study Type: Verification and Validation Activities, including performance testing of Annotate Module, SmartFuse Module, and AdaptBox module, as well as non-clinical tests.

Sample Size:

Annotate Module: Sample sizes for individual organs range from 20 to 38, always above the minimum required sample size of 15 or 20.
SmartFuse Module: Sample sizes for clinical uses are 30 and 45, above the minimum required sample size of 15 or 17.
AdaptBox Module (Synthetic-CT from CBCT): Sample size 20, above the minimum required 17.
AdaptBox Module (Dose engine): Sample size 272 (Brain: 42, H&N: 70, Chest: 44, Breast: 26, Pelvis: 90).

AUC: Not found.

MRMC: Not found.

Standalone Performance: The device is a standalone software application. Performance studies evaluate the accuracy of its functions, such as auto-segmentation, registration, and synthetic-CT generation.

Key Results:

Annotate Module (Autosegmentation):
- Passed intervariability comparisons (DICE diff inter-expert ranging from 1.19% to 2.44% for bowel structures).
- Passed qualitative evaluations (A+B% metrics ranging from 92.10% to 100% for various organs, always above the 85% criterion).
SmartFuse Module (Registration):
- Rigid registration: A+B% = 95.56% for tCBCT - sCT, A+B% = 70.37% for tCT - sSCT.
- Deformable registration: A+B% = 97.78% for tCBCT - sCT, A+B% = 94.06% for tsynthetic-CT - sCT. All passed acceptance criteria (>=50% for rigid, >=85% for deformable).
AdaptBox Module (Synthetic-CT from CBCT):
- Gamma index: 2%/2mm = 98.85%, 3%/3mm = 99.43%.
- DVH parameters (PTV):

Regulation Number and Section

§ 892.5050 Medical charged-particle radiation therapy system.

(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.

Summary

0

December 22, 2023

Image /page/0/Picture/1 description: The image shows 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" in a square and the words "U.S. FOOD & DRUG ADMINISTRATION".

TheraPanacea % Bhairavi Ajachandra QA/RA Manager 7 bis boulevard Bourdon Paris. 75004 FRANCE

Re: K232479

Trade/Device Name: ART-Plan Regulation Number: 21 CFR 892.5050 Regulation Name: Medical Charged-Particle Radiation Therapy System Regulatory Class: Class II Product Code: MUJ Dated: November 20, 2023 Received: November 20, 2023

Dear Bhairavi Ajachandra:

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 (the 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 available 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.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming

1

product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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,

Locon Weidner

Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team 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

2

Indications for Use

510(k) Number (if known) K232479

Device Name ART-Plan

Indications for Use (Describe)

ART-Plan's indicated target population is cancer patients for whom radiotherapy treatment has been prescribed. In this population, any patient for whom relevant modality imaging data is available.

ART-Plan is not intended for patients less than 18 years of age.

The indicated users are trained medical professionals including, but not limited to, radiotherapists, radiation oncologists, medical physicists, dosimetrists and medical professionals involved in the radiation therapy process.

The indicated use environments are, but not limited to, hospitals, clinics and any health facility involved in radiation therapy.

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.

Level of documentation:

Enhanced

Submitter's Name:

TheraPanacea SAS

Submitter's Address:

7 bis boulevard Bourdon 75004 Paris France

Telephone: +33 9 62 52 78 19

Establishment Registration Number:

3019834893

Contact Person:

Bhairavi Ajachandra

Telephone: +33 (0) 620604982

Date Prepared:

10 Aug 2023

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

Primary Product Code:

| Regulation
Number | Device | Device
Class | Product
Code | Classification
Panel |
|----------------------|------------------------------------------------------|-----------------|-----------------|-------------------------|
| 892.5050 | Medical charged-particle
radiation therapy system | Class II | MUJ | Radiology |

Device Trade Name:

ART-Plan

Device Common Name:

ART-Plan

4

Intended Use:

ART-Plan is a software intended to be used by trained clinicians who are familiar with radiation therapy, such as medical physicists, medical dosimetrists and radiation oncologists. The software consists of different applications, each used for specific purposes at a different phase of radiation treatment planning.

ART-Plan offers the following tools to aid in the workflow of radiotherapy treatment:

Multi-modal visualization and rigid- and deformable registration of anatomical and . functional images such as CT, MR, PET-CT, 4D-CT, CBCT and synthetic-CT generated from CBCT
Display of fused and non-fused images to facilitate the comparison and delineation of image data by the user
Manual generation, 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 ● on medical practices, on medical images such as CT and MR images
Generation of synthetic-CT from MR images for supported anatomies
Generation of synthetic-CT from CBCT images for supported anatomies ●
Dose computation on CT and/or synthetic-CT images for external beam irradiation ● with photon beams
Assisted CBCT-based off-line adaptation decision-making for supported anatomies

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

Indications for Use:

ART-Plan's indicated target population is cancer patients for whom radiotherapy treatment has been prescribed. In this population, any patient for whom relevant modality imaging data is available.

ART-Plan is not intended for patients less than 18 years of age.

The indicated users are trained medical professionals includinq, but not limited to, radiotherapists, radiation oncologists, medical physicists, dosimetrists and medical professionals involved in the radiation therapy process.

The indicated use environments include, but are not limited to, hospitals, clinics and any health facility involved in radiation therapy.

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Summary of Substantial Equivalence:

The following predicate devices have been that the ART-Plan can claim equivalence with and these are detailed below in Table 1. Summary of Substantial Equivalence.

General Comparison

	General Information
Property	Proposed Device
ART-Plan v2.1.0	Primary Predicate
Ethos Treatment
Management, 2.1;
Ethos Treatment
Planning, 1.1	Reference device
ART-Plan
v1.10.1	Reference
device
ECLIPSE
WITH AAA	Reference
device
Eclipse Treatment
Planning System	Comment
Common
Name	System, Planning,
Radiation Therapy
Treatment	accelerator, linear,
medical	Radiological image
processing software
for radiation therapy	System, Planning,
Radiation Therapy
Treatment	System, Planning,
Radiation Therapy
Treatment	The proposed device shares the same
common name "System, Planning,
Radiation Therapy Treatment" with the
primary predicate, especially Ethos
Treatment Planning, 1.1 (that has
"MUJ" as a product code in its 510(k)
summary) and with some of the
reference devices.
Device
Manufactur
er	TheraPanacea SAS	Varian Medical
Systems, Inc	TheraPanacea SAS	Varian Medical
Systems
(now
Varian Medical
Systems, Inc)	Varian Medical
Systems, Inc	N/A
510k	N/A	K212294	K230023	K041403	K102011	N/A
Device
Classificatio
n	II	II	II	II	II	N/A
The proposed device, primary
predicate and reference devices
have identical device classification.
Primary
Product
Code	MUJ	IYE, MUJ	QKB	MUJ	MUJ	The primary product code is MUJ for
"System, Planning, Radiation Therapy
Treatment" as it is a software used in
the planning of radiotherapy treatment
like the primary predicate, especially
Ethos Treatment Planning, 1.1 (that
has "MUJ" as a product code in its
510(k) summary) and some reference
devices use it as their primary or
secondary code
Secondary
Product
Code	QKB, LLZ	LLZ, MUJ		LHN	As secondary product code:

QKB (Radiological image
processing software for radiation
therapy) has been included as the
software uses Al algorithms and is
intended for radiation therapy. It is
also the primary code of the
reference device ART-Plan
v1.10.1 of which ART-Plan 2.1.0 is
an update;
LLZ (System, Image Processing,
Radiological) has been included
as the software is used in image
processing and it is also the
subsequent code of the reference
device ART-Plan v1.10.1 of which
ART-Plan 2.1.0 is an update. | |
| Target
Population | ART-Plan's
indicated target
population is cancer
patients for whom
radiotherapy
treatment has been
prescribed. In this
population, any
patient for whom
relevant modality
imaging data is
available. | The patient target
groups are the
patients for whom
radiation therapy is
indicated. | ART-Plan's indicated
target population is
cancer patients for
whom radiotherapy
treatment has been
prescribed. In this
population, any patient
for whom relevant
modality imaging data
is available. | Not stated | Any patients with
malignant or benign
diseases | The proposed device and the primary
predicate have identical target
populations. |
| Environmen
t | Hospital | Hospital | Hospital | Hospital | Hospital | The proposed device, primary
predicate and reference devices
have identical target environments. |
| Intended
Use/
Indication
for Use | Intended Use
ART-Plan is a
software intended to
be used by trained
clinicians who are
familiar with
radiation therapy,
such as medical
physicists, medical
dosimetrists and | Intended Use
Ethos Treatment
Management is
used to manage
and monitor
radiation therapy
treatment plans
and sessions; it is
intended to be used | Intended Use
ART-Plan is a
software for
multi-modal
visualization,
contouring and
processing of 3D
images of cancer
patients for whom | Intended Use
The Varian Eclipse
device is a
treatment planning
system used for
diagnostic image
analysis,
contouring and
segmentation,
geometrical | Intended use
Not available in the
summary:

Indication for use
The Eclipse
Treatment Planning
System (Eclipse
TPS) is used to
plan radiotherapy | The intended use and indications
for use of the proposed device,
ART-Plan v2.1.0 and the primary
predicate (especially Ethos
Treatment Planning, 1.1) are
similar as they are both softwares
intended to be used in the planning
of radiotherapy treatment¹: |
| radiation
oncologists. The
software consists of
different
applications, each
used for specific
purposes at a
different phase of
radiation treatment
planning. | with a treatment
planning system.
Ethos Treatment
Planning is used to
generate and
modify radiation
therapy treatment
plans. | 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. | planning, photon
and electron dose
calculation and
plan review. | treatments for
patients with
malignant or benign
diseases. Eclipse
TPS is used to plan
external beam
irradiation with
photon, electron
and proton beamA,
as well as for
internal irradiation
(brachytherapy)
treatments. In
addition, the
Eclipse Proton Eye
algorithm specifically
indicated for
planning proton
treatment of
neoplasms of the
eye. | they allow multi-modal visualisation
rigid- and deformable
registration for the same modalities
of images (CT, MR and PET) | |
| ART-Plan offers the
following tools to aid
in the workflow of
radiotherapy
treatment:

Multi-modal
visualization and
rigid- and
deformable
registration of
anatomical and
functional images
such as CT, MR,
PET-CT, 4D-CT,
CBCT and
synthetic-CT
generated from
CBCT
Display of fused
and non-fused
images to
facilitate the
comparison and
delineation of
image data by the
user
Manual
generation,
modification and
semi-automatic
generation of
contours for the
regions of interest | Indications for
Use
Ethos Treatment
Management is
indicated for use in
managing and
monitoring
treatment plans
and sessions.
Ethos Treatment
Planning is
indicated for use in
generating and
modifying radiation
therapy treatment
plans. | ART-Plan offers the
following visualization,
contouring and
manipulation tools to
aid in the preparation
of radiotherapy
treatment: | Indication for use
The Varian Eclipse
device is used to
plan photon and
electron radiation
therapy treatments
employing linear
accelerators and
other similar
teletherapy
devices with x-ray
energies from 1-50
MV, as well as
Cobalt-60, and
electron energies
from 1-50 MeV.
Eclipse will plan
the 3D
radiotherapy
treatment
approaches to
combined modality
plans, coplanar
and non-coplanar
fields, static and
ARC fields, beam
modifiers, and
beam intensity
modulators.
Eclipse also
includes tools for
treatment
preparation
(diagnostic image
and analysis,
contouring and
segmentation) and
plan review. | | they allow displaying fused and
non-fused images to facilitate the
comparison and delineation of image
data by the user

they allow manual generation,
modification and semi-automatic
generation of contours for the
regions of interest

they allow automatic segmentation
on medical images using AI
algorithms

they allow generation of synthetic-CT
from CBCT images

they allow dose computation on CT
and/or synthetic-CT images for
external beam irradiation with photon
beams

they allow assisted CBCT-based
off-line adaptation decision-making
for supported anatomies

they allow the import, manipulation,
visualisation, generation and the
export of DICOM images

The intended use 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 modality
for the same claim as compared to
the primary predicate as: | |
| | | - Multi-modal
visualization and rigid-
and deformable
registration of
anatomical and
functional images | | | | |

6

1 Information found on the previous 510(k) summaries, labelling and its manufacturer's (Varian Medical Systems, Inc) website.

7

8

| 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 | such as CT, MR,
PET-CT, 4D-CT and
CBCT

Display of fused and
non-fused images to
facilitate the
comparison and
delineation of image
data by the user
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
on medical practices,
on medical images
such as CT and MR
images.
Generation of
pseudo-CT for
supported anatomies | - it can generate synthetic CT from
CBCT and MR images whereas it is
not possible with Ethos Treatment
Planning, 1.1 to do so with MR
images. This does not represent an
additional claim as the technological
characteristics are the same and it
does not raise different questions of
safety and effectiveness. Also, this
feature is covered by the reference
device ART-Plan v1.10.1, which is
the previous version cleared of the
proposed device ART-Plan v2.1.0. |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Generation
of
synthetic-CT from
MR images for
supported
anatomies | The device is intended
to be used in a
radiation therapy
clinical setting, by
trained professionals
only. | |
| Generation
of
synthetic-CT from
CBCT images for
supported
anatomies | | |
| Dose computation
on CT and/or
synthetic-CT
images for
external beam
irradiation with
photon beams | | |
| Assisted
CBCT-based
off-line adaptation
decision-making
for supported
anatomies | | |
| 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. | |
| population is cancer
patients for whom
radiotherapy
treatment has been
prescribed. In this
population, any
patient for whom
relevant modality
imaging data is
available. | 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 DICOM 3.0
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 | |
| ART-Plan is not
intended for
patients less than
18 years of age. | | |
| The indicated users
are trained medical
professionals
including, but not
limited to,
radiotherapists,
radiation
oncologists, medical
physicists,
dosimetrists and
medical
professionals
involved in the
radiation therapy
process. | | |
| The indicated use
environments
include, but are not
limited to, hospitals,
clinics and any
health facility
involved in radiation
therapy. | | |

Page 6 of 31

9

10

| | 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. | | | |

11

System Information Comparison

	System Information
Property	Proposed Device
ART-Plan v2.1.0	Primary Predicate
Ethos
Treatment
Management, 2.1;
Ethos
Treatment
Planning, 1.1	Reference device
ART-Plan
v1.10.1	Reference
device
ECLIPSE WITH
AAA	Reference device
Eclipse Treatment
Planning System	Comment
Method
of
Use	Standalone
software application
accessed via a
compliant browser
(Chrome, Mozilla
Firefox and Edge)
on a
personal
computer, tablet or
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
communication
device.
The
platform is optimally
used with 17 inches
and up screen.
Facilitates display
and visualization of
data by user.	Standalone software
device	Standalone
software application
accessed via a
compliant
browser
(Chrome or Mozilla
Firefox) on a
personal computer,
tablet or 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
communication
device. The platform
is optimally used
with 17 inches and
up screen.
Facilitates display
and visualization of
data by user.	Computer based
software device	Computer based
software device	The proposed device and the primary
predicate are both standalone
software. More details have been
found on the reference device
ART-Plan v1.10.1 which has identical
methods of use than the proposed
device. An improvement has been
introduced with ART-plan v2.1.0 as it
can be also used on Edge browser.
Data
Visualization
/
Graphical
Interface	Yes	Yes	Yes	Yes	Yes	The proposed device, the primary
predicates and all references devices
have a data visualisation and graphical
interface
Synthetic
CT	Generation of CT
density image
series out of
multiple MR-image
series and CBCT
images	Generation of CT
density image series
out of CBCT images	Generation of CT
density image
series out of
multiple MR-image
series	N/A	N/A	The proposed device and primary
predicate can generate synthetic-CT
from CBCT image.
The proposed device can generate
synthetic CT from CBCT and MR
images whereas it is not possible with
						the primary predicate to do so with MR images. This does not represent an additional claim as the technological characteristics are the same and it does not raise different questions of safety and effectiveness. Also, this feature is covered by the reference device ART-Plan v1.10.1, which is the previous version cleared of the proposed device ART-Plan v2.1.0.
Dose computation	Dose computation on CT and/or synthetic-CT images for external beam irradiation with photon beams	Dose calculation with AAA dose calculation model and Acuros XB dose calculation algorithm on CT and / or synthetic CT images	N/A	The AAA dose calculation model is a 3D convolution/superposition algorithm that models primary photons, photons scattered in the medium, contamination electrons and transport electrons near tissue heterogeneities. The AAA dose calculation model is comprised of two main components, one being the configuration algorithm and the other one the actual dose calculation algorithm.	AcurosXB dose calculation algorithm	The proposed device, the primary predicate and some reference devices ECLIPSE WITH AAA and Eclipse Treatment Planning System can perform dose computation.
Off-line adaptation decision-making	Assisted CBCT-based off-line adaptation decision-making for supported anatomies	Ethos Treatment Management allows the physician to do initial planning, review and approve candidate plans, and monitor ongoing treatments. Support for adaptive radiotherapy	N/A	N/A	N/A	The proposed device and the primary predicate can assist off-line adaptation decision-making.
		treatment planning
and automated plan
generation
Supported
Modalities	Registration:
Static and gated CT,
MR, PET (via the
registration of the
CT of said PET),
4D-CT, CBCT and
synthetic-CT
generated from
CBCT
Segmentation:
CT (injected or not),
MR images, DICOM
RTSTRUCT,
synthetic-CT from
CBCT	Registration:
CT
(including
synthetic CT from
CBCT), MR and
PET
Segmentation:
CT and synthetic CT
from CBCT	Registration:
Static and gated CT
(including 4D-CT
and CBCT), MR,
PET (via the
registration of the
CT of said PET)
Segmentation:
CT (injected or not),
MR images, DICOM
RTSTRUCT	Segmentation:
Eclipse also includes
tools for treatment
preparation
(diagnostic image
and analysis,
contouring and
segmentation) and
plan review.	Registration:
CT/MR/PET Image
Registration
4D image display
(registration of time
yes yes series of 3D
images)
Segmentation:
Geometrical shapes,
Manual editing and
manipulation tools,
Automatic
/semi-automatic
tools,
Automatic/semi-auto
matic on-demand
and post-processing
tools for individual
organs/structures,
Automatic on-demand
and pre-processing tools
for multiple
organs/structures, 3D
Autornargin, Logical
operators	The proposed device, the primary
predicate and most of the reference
devices propose both registration and
segmentation on medical images of
different modalities.
Data Export	Distribution of
DICOM compliant
Images into other
DICOM compliant
systems.	ARIA RadOnc
integration, DICOM
RT, other image
formats, eclipse
scripting API
(ESAPI) read only
access, eclipse
scripting API
(ESAPI) write
access, Eclipse
automation, Export
field coordinates to
laser system	Distribution of
DICOM compliant
Images into other
DICOM compliant
systems.	DICOM including RT
objects, MDC shaper
files, blocks and
compensator data to
Par Scientific, plan
and dose data to
picker AcQSim,
integrated with Varis
verification, ASCIIfile
to laser system,
Varian CadPlan plus
6.0	VARIS/Vision
database integration,
DICOM RT/3.0, other
image formats,
export field
coordinates to laser
system	The proposed device, the primary
predicate and reference devices have
identical data export capabilities with
DICOM format.
		RT prescription
information available
Compatibility	Compatible with
data from any
DICOM compliant
scanners for the
applicable
modalities.	ARIA RadOnc
integration, DICOM
RT, other image
formats,
electromagnetic
digitizer, eclipse
scripting API
(ESAPI) read only
access, eclipse
scripting API
(ESAPI) write
access, eclipse
automation, Basic
RT prescription
information available	Compatible with
data from any
DICOM compliant
scanners for the
applicable
modalities.	DICOM including RT
objects, CART
format, TIFF format,
CMP format,
Configurable pure
pixel data,
PortalVision MArk 1
& 2, Varian CT
option,
Electromagnetic
Digitilizer, Film
scanner	VARIs/Vision
database integration,
DICOM RT/3.0, other
image formats,
Electromagnetic
digitizer, film scanner	The proposed device, the primary
predicate and reference devices have
identical compatibility (DICOM format)

Page 9 of 31

12

Page 10 of 31

13

Page 11 of 31

14

Technical Information Comparison

		Technical Information
Property	Proposed Device
ART-Plan v2.1.0	Primary Predicate
Ethos Treatment
Management, 2.1;
Ethos Treatment
Planning, 1.1	Reference device
ART-Plan
v1.10.1	Reference
device
ECLIPSE WITH
AAA	Reference device
Eclipse Treatment
Planning System	Comment
Delineation
Method	Al	Al	Al	Not stated	Not stated	The proposed device, primary predicate and
one of the reference devices (ART-Plan
v1.10.1) 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/	Registration:
CT (including
synthetic CT from
CBCT), MR and
PET	Multi-modal and
mono-modal.
Rigid and deformable
Automatic and
manual initialization
(landmarks, fusion
box, alignment).
Registration for the
purposes of	NA	CT/MR/PET Image
Registration
4D image display
(registration of time
yes yes
series of 3D images)	The proposed device, the primary predicate and
most of the reference devices propose
registration of medical images of different
modalities.
	recontouring and
AI-based automatic
contouring.	CT and synthetic CT
from CBCT	replanning/
recontouring
and
AI-based automatic contouring.
Segmentatio
n 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

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) | Eclipse also
includes tools for
treatment
preparation
(diagnostic image
and analysis,
contouring and
segmentation) and
plan review. | Geometrical shapes,
Manual editing and
manipulation tools,
Automatic
/semi-automatic
tools,
Automatic/semi-auto
matic on-demand
and post-processing
tools for individual
organs/structures,
Automatic
on-demand and
pre-processing tools
for multiple
organs/structures, 3D
Autornargin, Logical
operators | The proposed device, the primary predicate and
most of the reference devices propose
segmentation on medical images of different
modalities using Al. |
| View
Manipulation
and
Volume
Rendering | Window and level,
pan, zoom,
cross-hairs, slice
navigation.

Color rendering, fused
views, gallery views. | Window and level,
pan, zoom,
cross-hairs, slice
navigation.

Color rendering,
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 | The proposed device has the same tools as the
primary predicate. |
| Regions and
Volumes
of Interest
(ROI) | AI
Based
autocontouring,
Registration based
contour projection
(re-contouring),
Manual
manipulation and
transformation
(margins, booleans
operators,
interpolation). | AI based
autocontouring,
Registration based
contour projection
(re-contouring)
Manual
manipulation and
transformations
(margins, booleans
operators,
interpolation). | AI
Based
autocontouring,
Registration based
contour projection
(re-contouring),
Manual
manipulation and
transformation
(margins, booleans
operators,
interpolation). | Not stated | Not stated | Both the proposed device and the primary
predicate allow AI automatic contouring and
manual contouring. |
| Region/volu
me of
interest
measuremen
ts and
size
measuremen
ts | Intensity
and
Hounsfield units.
Size
measurements
include 2D
and 3D
measurements
(number of
slices,
volume of a structure,
static ruler) | Intensity, Hounsfield
units.
Size measurements
include 2D and 3D
measurements
(number of
slices,
volume of a
structure,
static
ruler) | Intensity, Hounsfield
units, and SUV
Size measurements
include 2D and 3D
measurements
(number of slices,
volume of a structure,
static ruler) | Not stated | Not stated | The proposed device offers the same kind of
region/volume of interest
measurements and size measurements as the
primary predicate. |

15

16

17

Device Description:

The ART-Plan application consists of three key modules: SmartFuse,Annotate and AdaptBox, allowing the user to display and visualise 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.

Compared to Ethos Treatment, 2.1; Ethos Treatment Planning, 1.1 (primary predicate), the following additional feature has been added to ART-Plan v2.1.0:

generation of synthetic CT from MR images. This does not represent an additional . claim as the technological characteristics are the same and it does not raise different questions of safety and effectiveness. Also, this feature is already covered by reference and previous version of the device ART-Plan v1.10.1.

| Generation of
Synthetic CT | ART-Plan V2.1.0
(Proposed device) | Ethos Treatment
Management, 2.1;
Ethos Treatment
Planning, 1.1
(primary
predicate) | ART-Plan v1.10.1
(reference device and
previous version of the
proposed device) |
|-------------------------------|----------------------------------------|---------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|
| from MR images | ✓ | | ✓ |
| from CBCT images | ✓ | ✓ | |

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 addina/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 and CBCT images.

18

. Allowing the users to generate, visualize and analyze dose on images of CT modality (only within the AdatpBox workflow)
. Presenting to the user metrics to define if there is a need for replanning or not.

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, on synthetic-CT from CBCT and on 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

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

· pelvis male

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.

Information about our training dataset and generalizability of the models:

A method generalizes well if the observed performance on training and validation sets remains stable. In the case of strong presence of expert's annotation variability (that is not necessarily because of erroneous annotations but because image quality/orqan visibility can be interpreted differently among experts), a method that can demonstrate similar performance with respect to a given metric on training, validation and later on testing is considered to generalize well.

In that process, both the loss function being optimized by the optimization procedure (stochastic gradient descent) and the dice metric which is the main proxy of segmentation quality, are monitored over the train and validation sets. If the loss is non-increasing on the validation set and if the dice metrics follow similar in value trends in both the validation and training sets, it is considered that the model being trained does not overfit, and hence should generalize well, at least on input domains similar to ones in those sets.

On the contrary, overfitting can be detected whenever the training loss keeps decreasing while the validation loss after a while increases. This means that the model is focusing on features that are specific to the training data and not present in the validation data. This implies that the capacity of the model to generalize is poor. In that respect, the independence of the train/validation/test sets is fundamental.

We consider that a model is a good candidate for production when the following conditions are met: 1) the loss and dices have reached a plateau on the validation set, 2) there is no overfitting, i.e. training and validation curves are similar and 3) the level of the dice for the different organs are as good or above the clinical expectations according to well defined performance criteria.

19

The learning curves of organs may be different depending on the sizes and shapes (difficulties) of structures (organs). Thus, the range of testing scores, Dice Similarity Coefficient (DSC), may vary. It is important to remember that smaller orqans might have smaller DSC and yet be still clinically relevant and acceptable, as the DSC is a relative metric that is heavily dependent on the volume of the organ. This is due to the fact that the DSC scores are normalized from the union of organ volume between the two sets (ground truth, automatic annotations) and therefore lower DSC could correspond to clinically acceptable values for small organs, since the proposed contours might take just a few editions to make them usable for planning, whilst still saving time from the users, i.e. that these contours would be judged "clinically acceptable after minor corrections" in a qualitative evaluation.

Learning curves can have an average DSC and loss function for each epoch (which is an iteration of training where the whole training dataset has been passed to the network) over the training set and over the validation set. Our curves show that validation and training data are very close to each other, reaching convergence after some epochs (depending on the structure), demonstrating no overfitting of the training data. Once convergence is achieved, the model is considered ready to be tested and clinically validated on a different, yet representative data set, following a well-established process of validation that has already been submitted to and cleared by the FDA.

Some limitations have been identified that correspond either to the sex or the age of patients. For instance, for the auto-segmentation model following limitations are disclosed to the user in the Instruction For Use (User Manual) based on the sex of the patient:

-The Truefisp Pelvis MRI and T2 Elekta Pelvis MRI auto-contouring models only work on male anatomy.
-The patient sex of the patient (dicom tag (0010, 0040)) is taken into account for the auto-segmentation:
- if the tag is "F" or "M", the sex specific organs (prostate, breast, etc.) are contoured according

to the tag

if the tag is emptv or "O":
- ' if batch: no contour is delineated except external contour
  - if auto segmentation on Annotate: only common contours to the 2 sexes are delineated
if the tag is incorrect, the generated contours may be inappropriate -

The automatic contouring (including external contour) function may generate inappropriate contours in the following cases:

When the volume used is an image taken of a child -
-When the patient has a particular anatomy.
When the considered volume is that of a patient not positioned on his back at the time of acquisition.
-When the value entered in the Patient Position tag (0018, 5100) is erroneous.
-When the DICOM-CT contains an unusually high number of slices.
When the quality of the images used as input is not satisfying enough or the resolution is low such as CBCT. Therefore, the contours produced may have a low quality.
-When the primary volume is an MRI whose acquisition sequence is not compatible with the selected auto-contouring model.

20

When the patient is unusually positioned on the image (image not centered on the patient, head rotated on the side ... )
Only some anatomies are covered by the automatic contouring:
Automatic contouring on CT images covers all anatomies (Head & Neck, thorax. breast, abdominal region and pelvis (M/F)
-Automatic contouring on MR images covers some sequences and anatomies: Brain T1, Abdo TF (TrueFisp), Pelvis (male) T2, Pelvis (male) TF.
-Automatic contouring on synthetic-CT from CBCT covers pelvis (male) anatomy.
-In order to suggest the most relevant structures to the user, a CT that does not include a chiasma but does include a liver, is not considered as Head and Neck case. In that case, no Head and Neck structures will be automatically segmented.

All information on the limitations of some models is included in the Instruction For Use (User Manual) which is made available to all users of the software.

. Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized 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 SmartFuse module, the acceptance criteria are as follows:

1. ART-Plan SmartFuse produces an anatomical registration of a source image towards a target image for which:
- a) Dice Similarity Coefficient (DSC) of the segmented registered and non registered image is equal to or superior to the acceptance criteria set by the AAPM: DSC(mean)≥0.81
- b) Dice Similarity Coefficient (DSC) of the segmented registered and non registered image is equal to or superior to a benchmark device: DSC(mean)≥0.65
- c) The clinicians' qualitative evaluation of the propagated contours post-registration are considered acceptable for clinical use without

21

modifications (A) or with minor modifications/corrections (B) with A+B% above or equal to 85% for deformable and above or equal to 50% for rigid registration considering the following scale:

A: the contour is acceptable for a clinical use without any modification i)
B: the contour would be acceptable for a clinical use after minor ii) modifications/corrections
C: the contour requires major modifications (e.g. it would be faster for iii) the expert to manually delineate the structure)
d) The clinicians' qualitative evaluation of the overall registration output following clinical protocols to qualitatively assess registration outcomes is considered acceptable for clinical use with A+B% above or equal to 85% for deformable and 50% for rigid registration considering the following scale:
- A: the registration exceeds the expectation i)
- B: the registration meets the expectation (incl. cases for which ii) additional margin may be required or registration might be relaunched using different supporting tools)
- C: the registration is not acceptable iii)
1. ART-Plan SmartFuse module produces an anatomical registration of a source image towards a target image for which the Jacobian Determinant need to be positive
1. ART-Plan SmartFuse module produces an anatomical registration of a source image towards a target image for which the target registration error (TRE) must be bellow the acceptance criteria set by the AAPM (maximum voxel size of the pair of images involved): TRE