Predicate Devices

Reference Devices

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The summary explicitly states that the device uses a "deep learning method for automatic contouring of structure delineation" and has an "Automatic CT segmentation tool: Yes (deep learning)". Deep learning is a subset of machine learning.

Therapeutic

No
The device is a treatment planning system used to plan radiotherapy, not to administer or deliver therapy itself. Therefore, it is not a therapeutic device.

Diagnostic

No

The MOZI Treatment Planning System (MOZI TPS) is described as a system used to plan radiotherapy treatments. Its functions like image processing, structure delineation, plan design, optimization, and evaluation are all geared towards planning a treatment rather than identifying or diagnosing a disease.

SaMD (Software as a Medical Device)

Yes

The device description explicitly states it is a "standalone software" and its functions are entirely software-based (image processing, structure delineation, plan design, optimization, evaluation, user interface, management). 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, the MOZI Treatment Planning System (MOZI TPS) is not an In Vitro Diagnostic (IVD) device.

Here's why:

Definition of IVD: In Vitro Diagnostic devices are used to examine specimens derived from the human body (like blood, urine, tissue) to provide information for diagnosis, monitoring, or screening.
MOZI TPS Intended Use: The MOZI TPS is used to plan radiotherapy treatments for patients with malignant or benign diseases. This is a treatment planning tool, not a diagnostic tool that analyzes biological samples.
Device Description: The description focuses on image processing, structure delineation, plan design, optimization, and evaluation for radiotherapy. While it uses medical images, it doesn't analyze biological specimens.
Input Modalities: The input modalities are various medical imaging techniques (CT, MRI, etc.), which are used for visualizing the patient's anatomy and disease, not for analyzing biological samples.

The MOZI TPS is a medical device used in the planning phase of radiation therapy, which is a treatment modality. It does not perform diagnostic tests on samples taken from the body.

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

The MOZI Treatment Planning System (MOZI TPS) is used to plan radiotherapy treatments with malignant or benign diseases. MOZI TPS is used to plan external beam irradiation with photon beams.

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

MUJ

Device Description

The proposed device, MOZI Treatment Planning System (MOZI TPS), is a standalone software which is used to plan radiotherapy treatments (RT) for patients with malignant or benign diseases.

Its core functions include image processing, structure delineation, plan design, optimization and evaluation. Other functions include user login, graphical interface, system and patient management. It can provide a platform for completing the related work of the whole RT plan.

The product uses generally accepted methods for below automatic function:
automatic rigid and deformable registration of image processing
dose calculation with Monte Carlo method of plan design and optimization

And it also uses deep learning method for automatic contouring of structure delineation.

Mentions image processing

Yes

Mentions AI, DNN, or ML

Yes

Input Imaging Modality

Not Found

Anatomical Site

Head & Neck, Brain, Thorax (Esophageal, Lung), Abdomen (Gastrointestinal), Male Pelvis (Prostate), Female Pelvis

Indicated Patient Age Range

Patient ages range 21-30 : 0.3%, 31-50 : 31%, 51-70 : 51.3%, 71-100 : 14.4%.

Intended User / Care Setting

Not Found

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

For CT structure models there were 560 training. The training image set source is from China.

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

For CT structure models there were 187 testing image sets. The testing image source is from the United States. They are independent of each other.

The test data set information is as follows:
(1) Among the patients used for CT testing 57% were male and 43% female. Patient ages range 21-30 : 0.3%, 31-50 : 31%, 51-70 : 51.3%, 71-100 : 14.4%. Race 78% White, 12% Black or African American, 10% Other.
(2) CT datasets spanned across treatment subgroups most typically found in a radiation therapy treatment clinic with the most common diagnosis being cancers of the Head and Neck (20.3%), Esophageal and Lung (Thorax, 20.3%), Gastrointestinal (Abdomen, 20.3%), Prostate (Male Pelvis, 20.3%), Other (Female Pelvis, 18.7%).
(3) The images were obtained using scanners supplied by GE/Philips/Siemens, including 28.3% by GE, 33.7% by Philips and 38% by Siemens. And the images contained different slice thicknesses, distributed as follows: 5.3% 1mm, 28.3% 2mm, 2.7% 2.5mm, 23% 3mm, 40.6% 5mm slice thickness.

Ground truthing of test data set was generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists.

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

Commissioning test of Intensity-modulated radiation treatment planning system: The commissioning test was performed to verify the IMRT/VMAT performance of the subject device according to AAPM TG-119 Report. The Monte Carlo dose computation algorithm in subject device has been successfully validated for accuracy in clinically relevant settings according to specification.

Radiotherapy Treatment Planning System Software performance test: The performance test was performed on subject device and predicate device (K172163) to evaluate the performance in formulating external irradiation photon therapy plan. The subject device and predicate device both support Dose Calculation feature, the subject device incorporates a Monte Carlo based algorithm while the predicate device an AAA/AXB based algorithm. However, Monte Carlo、AAA、AXB are all universally acknowledged algorithm on dose calculation. And the algorithm used in subject device has been verified for its performance comparing against that of the predicate device, the result demonstrated that they have equivalent performance.

Deformable registration performance test: The automatic deformable registration algorithm performance test was performed against the reference device (K182624) to evaluate the deformable registration accuracy. All fixed images and moving images were generated in healthcare institutions in U.S. The scanner models covered five major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. The Normalized Mutual Information (NMI) and Hausdorff Distance (HD) were used for evaluation. NMI and HD values were calculated on two sets of images for both the proposed device and predicate device (K182624). respectively. The NMI and HD values of the subject device was compared with that of the predicate device. According to the results, it could be concluded that the NMI and HD values of the proposed device was non-inferiority compares with that of the predicate device.

Standards conformance test: A verification test was performed on subject device to verify the compliance of IEC 62083-2009 Standard. It is verified that the subject device has met the requirements for the safety of radiotherapy treatment planning systems.

End-to-end test: We conducted end-to-end test for 18 patients' treatment planning including the Simulation CT images import to the TPS, details of the contouring and image registration, step by step processes of the treatment planning and the optimization for IMRT and VMAT treatments, dose calculations, exporting the treatment plans to the LINAC software for delivery, and validation of the TPS with phantom measurement. The treatment planning in the test contains the cases with lung, liver, brain, and Head & Neck cancers using the IMRT and VMAT techniques. The test results are all passed that the entire workflow for the treatment planning have been adequately validated prior to the treatment of patients.

Structure delineation performance test: Mean Dice Similarity Coefficient (DSC) was used to evaluate the performance of Segmentation Function. The segmentation function of subject device has been verified for its performance comparing against that of the reference device K191928, by the DSC assessment method. The result demonstrated that they have equivalent performance. The DSC values of the subject device' s auto segmentation on the testing image set with 38 sample size are provided in the Key Metrics section.

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

Mean Dice Similarity Coefficient (DSC) values for automatic segmentation on a test set of 38 samples:

Body Part	OAR	Mean DSC values	Mean standard deviation
		Head&Neck	Brain
Brainstem	0.88		0.03
BrachialPlexus_L	0.61		0.05
BrachialPlexus_R	0.64		0.05
Esophagus	0.84		0.02
Eye-L	0.93		0.02
Eye-R	0.93		0.02
InnerEar-L	0.78		0.06
InnerEar-R	0.82		0.04
Larynx	0.87		0.02
Lens-L	0.77		0.07
Lens-R	0.72		0.08
Mandible	0.90		0.02
MiddleEar_L	0.73		0.04
MiddleEar_R	0.74		0.04
OpticNerve_L	0.61		0.07
OpticNerve_R	0.62		0.08
OralCavity	0.90		0.03
OpticChiasm	0.64		0.10
Parotid-L	0.83		0.03
Parotid-R	0.83		0.04
PharyngealConstrictors_U	0.87		0.03
PharyngealConstrictors_M	0.88		0.02
PharyngealConstrictors_L	0.87		0.03
Pituitary	0.74		0.14
SpinalCord	0.85		0.04
Submandibular_L	0.86		0.04
Submandibular_R	0.87		0.03
TemporalLobe_L	0.89		0.03
TemporalLobe_R	0.89		0.03
Thyroid	0.86		0.03
TMJ_L	0.79		0.06
TMJ_R	0.74		0.06
Trachea	0.90		0.02
Esophagus	0.80		0.05
Thorax	Heart	0.98	0.01
	Lung_L	0.99	0.00
	Lung_R	0.99	0.00
	Spinal Cord	0.97	0.02
	Trachea	0.95	0.02
Abdomen	Duodenum	0.64	0.05
	Kidney_L	0.96	0.02
	Kidney_R	0.97	0.01
	Liver	0.95	0.02
	Pancreas	0.79	0.04
	SpinalCord	0.82	0.02
	Stomach	0.89	0.02
Pelvic-Man	Bladder	0.92	0.03
	BowelBag	0.89	0.04
	FemurHead_L	0.96	0.02
	FemurHead_R	0.95	0.02
	Marrow	0.90	0.02
	Prostate	0.85	0.04
	Rectum	0.88	0.03
	SeminalVesicle	0.72	0.07
Pelvic-Female	Bladder	0.88	0.02
	BowelBag	0.87	0.02
	FemurHead_L	0.96	0.02
	FemurHead_R	0.95	0.02
	Marrow	0.89	0.02
	Rectum	0.77	0.04

Predicate Device(s)

K172163 (Eclipse Treatment Planning System (Eclipse TPS))

Reference Device(s)

K191928 (AccuContour™), K182624 (MIM-MRT Dosimetry)

Predetermined Change Control Plan (PCCP) - All Relevant Information

Not Found

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

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Manteia Technologies Co., Ltd. Chao Fang RA 1903-1904, B Tower, Zijin Plaza, No.1811 Huandao East Road Xiamen. Fujian 361001 CHINA

Re: K223724

Trade/Device Name: MOZI TPS Regulation Number: 21 CFR 892.5050 Regulation Name: Medical Charged-Particle Radiation Therapy System Regulatory Class: Class II Product Code: MUJ Dated: January 1, 2023 Received: January 3, 2023

Dear Mr. Chao Fang:

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

July 10, 2023

Image /page/0/Picture/9 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for 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,

Image /page/1/Picture/5 description: The image shows a digital signature. The signature is for Lora D. Weidner. The signature was created on July 10, 2023 at 17:31:17 -04'00'.

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

Enclosure

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Indications for Use

510(k) Number (if known) K223724

Device Name MOZI TPS

Indications for Use (Describe)

The MOZI Treatment Planning System (MOZI TPS) is used to plan radiotherapy treatments with malignant or benign diseases. MOZI TPS is used to plan external beam irradiation with photon beams.

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

X Prescription Use (Part 21 CFR 801 Subpart D)

| Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

The following information is provided as required by 21 CFR 807.92. The assign 510(k) Number: K223724

1. SUBMITTER

Name and Address:	Manteia Technologies Co., Ltd.
	1903-1904, B Tower, Zijin Plaza,
	No.1811 Huandao East Road,
	Xiamen, China
Establishment Registration
Number:	3016686005
Contact Person:	Chao Fang
	RA&QA Manager
	Email: fangchao@manteiatech.com
Date of Preparation:	July 9, 2023

2. DEVICE

Device/Trade Name:	MOZI TPS
Common Name:	MOZI Treatment Planning System (MOZI TPS)
Product Classification:	Class II
Classification Name:	System, Planning, Radiation Therapy Treatment
Product Code:	MUJ
Regulation Number:	21CFR 892.5050
Regulation Description:	Medical charged-particle radiation therapy system

3. PREDICATE DEVICE

Predicate Device:	K172163 (Eclipse Treatment Planning System (Eclipse TPS))
Reference Device 1:	K191928 (AccuContour™)
Reference Device 2:	K182624 (MIM-MRT Dosimetry)

4. Device Description:

The proposed device, MOZI Treatment Planning System (MOZI TPS), is a standalone software which is used to plan radiotherapy treatments (RT) for patients with malignant or benign diseases.

Its core functions include image processing, structure delineation, plan design, optimization and evaluation. Other functions include user login, graphical interface, system and patient management. It can provide a platform for completing the related work of the whole RT plan.

The product uses generally accepted methods for below automatic function:

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automatic rigid and deformable registration of image processing

dose calculation with Monte Carlo method of plan design and optimization
And it also uses deep learning method for automatic contouring of structure delineation.

5. INDICATIONS FOR USE

The MOZI Treatment Planning System (MOZI TPS) is used to plan radiotherapy treatments for patients with malignant or benign diseases. MOZI TPS is used to plan external beam irradiation with photon beams.

6. SUBSTANTIALLY EQUIVALENT(SE) COMPARISON

The Subject Device, MOZI TPS, makes use of a Predicate Device, Eclipse TPS(K172163), as the Predicate Device for substantial equivalence comparison, and Reference Device 1, AccuContour™(K191928), and Reference Device 2, MIM-MRT Dosimetry(K182624), as Reference Device for performance comparison on automatic function of rigid registration, deformable registration and contouring. In addition, the subject device adopts the same algorithm as AccuContour™(K191928) on automatic function of rigid registration and contouring.

At a high level, both the predicate device and the subject device are based on the same characteristics:

· Both the subject device and the predicate provide software tools for planning the treatment of malignant or benign diseases with radiation.

· They are computer-based software devices used by trained medical professionals to design and simulate radiation therapy treatments.

· They are both capable of planning treatments for external beam irradiation with photon.
The significant differences compared with the predicate device are as follows:
1. The intended use range of subject device is less than predicate device, which has been clearly stated in the intended use of subject device.
1. For dose calculation, the subject device incorporates a Monte Carlo based algorithm while the predicate device an AAA/AXB based algorithm. Both have been verified to be substantially equivalent by a performance test.
1. Performance test report on automatic rigid registration and contouring has been performed in subject device and reference device 1, AccuContour™(K191928). The test result is acceptable.
1. Performance test on automatic deformable has been performed in subject device and reference device 2, MIM-MRT Dosimetry(K182624). The test result is acceptable.

The following comparison table "Device Comparison Table" provides a detailed comparison.

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510(k) Summary

Device Comparison Table

| ITEM | Subject Device | Predicate Device
K172163 | Reference Device
K191928 | Reference Device
K182624 |
|----------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulatory Information | | | | |
| Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.2050 |
| Product Code | MUJ | MUJ | QKB | LLZ |
| Class | II | II | II | II |
| Indication for Use | The MOZI Treatment
Planning System (MOZI
TPS) is used to plan
radiotherapy treatments
for patients with
malignant or benign
diseases. MOZI TPS is
used to plan external
beam irradiation with
photon beams. | The Eclipse Treatment
Planning System (Eclipse
TPS) is used to plan
radiotherapy treatments
for patients with
malignant or benign
diseases. Eclipse TPS is
used to plan external
beam irradiation with
photon, electron and
proton beams, as well as
for internal Irradiation
(brachytherapy)
treatments.
In addition, the Eclipse
Proton Eye algorithm is
specifically indicated for
planning proton treatment
of neoplasms of the eye. | It is used by radiation
oncology department to
register multi-modality
images and segment
(non-contrast) CT
images, to generate
needed information for
Treatment planning,
treatment evaluation and
treatment adaptation. | MIM 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, US,
SPECT, PET and XA as supported by
ACR/NEMA DICOM 3.0. MIM
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.
• Localization and definition of
objects such as tumors and
normal tissues in medical
images. |
| Label/labeling | Conform with 21CFR
Part 801 | Conform with 21CFR
Part 801 | Conform with 21CFR
Part 801 | Conform with 21CFR
Part 801 |
| Operating System | Windows | Windows | Windows | Windows and MAC |
| Technological Characteristics | | | | |
| Dose calculation algorithms | Monte Carlo (photon) | AAA、AXB | N/A | N/A |
| Auto rigid registration algorithms | Intensity based | Intensity based | Intensity based | Intensity based |
| Auto deformable registration algorithms | Intensity based | Intensity based | Intensity based | Intensity based |
| Auto segmentation algorithms | Deep learning | Atlas based | Deep learning | Atlas based |
| Graphical User Interface Features | | | | |
| Multiple-instance application | Yes | Yes | N/A | N/A |
| Multiple-workspace layout | Yes | Yes | N/A | N/A |
| Graphical display/editing of field
parameters | Yes | Yes | N/A | N/A |
| Beam's-Eye-View display | Yes | Yes | N/A | N/A |
| 3D patient image display | Yes | Yes | N/A | N/A |
| Dose-Volume Histogram display | Yes | Yes | N/A | N/A |
| Patient Management Features | | | | |
| DICOM RT | Yes | Yes | N/A | N/A |
| (the device supports RT
Image/ RT Structure/ RT
Plan/ RT Dose) | | (the device supports RT
Image/ RT Structure/ RT
Plan/ RT Dose) | | |
| Import/Delete/Export/Edit/Retrieve
patient data | Yes | Yes | N/A | N/A |
| Image Processing Features | | | | |
| CT/MR manual image registration | Yes | Yes | Yes | Yes |
| Auto rigid registration | Yes (Intensity Based) | Yes (Intensity Based) | Yes (Intensity Based) | Yes (Intensity Based) |
| Auto deformable registration | Yes (Intensity Based) | Yes (Intensity Based) | Yes (Intensity Based) | Yes (Intensity Based) |
| Structure Delineation Features | | | | |
| Automatic CT segmentation tool | Yes (deep learning) | Yes (Atlas based) | Yes (deep learning) | Yes (Atlas based) |
| Manual CT segmentation tool | Yes | Yes | Yes | Yes |
| Plan Design & Plan Optimization Features | | | | |
| Photon Calculation | Yes | Yes | N/A | N/A |
| - Energy Range | 1 MeV – 20 MeV | 1 MeV – 50 MeV | N/A | N/A |
| - 3-dimensional conformal radiation
therapy Planning | Yes | Yes | N/A | N/A |
| - Intensity modulated radiotherapy
Planning | Yes | Yes | N/A | N/A |
| - Volumetric modulated arc therapy
Planning | Yes | Yes | N/A | N/A |
| - Directional heterogeneity
correction | Yes | Yes | N/A | N/A |
| - Treatment Head modelling | Yes | Yes | N/A | N/A |
| - Dose Dynamic Arc planning | Yes | Yes | N/A | N/A |
| - IMRT optimization | Yes | Yes | N/A | N/A |
| - VMAT optimization | Yes | Yes | N/A | N/A |
| - Dose Calculation | Yes (Monte Carlo) | Yes (AAA、AXB) | N/A | N/A |
| - Plan normalization | Yes | Yes | N/A | N/A |
| Plan Evaluation Features | | | | |
| - Create QA plan | Yes | Yes | N/A | N/A |
| - Plan evaluation tools | Yes | Yes | N/A | N/A |
| - Isodose levels display | 2D | 2D, 3D | N/A | N/A |
| - Reference point dose summary | Yes | Yes | N/A | N/A |
| - Dose Volume Histogram plot | Yes | Yes | N/A | N/A |
| - Plan summing tool | Yes | Yes | N/A | N/A |
| - Plan comparison tools | Yes | Yes | N/A | N/A |
| - Planar dose export | Yes | Yes | N/A | N/A |
| - Planning report output | Yes | Yes | N/A | N/A |
| - Graphics window screen dump | Yes | Yes | N/A | N/A |
| - Image set information | Yes | Yes | N/A | N/A |
| - Contouring Structural information | Yes | Yes | N/A | N/A |
| - Patient administration data | Yes | Yes | N/A | N/A |
| - Patient orientation | Yes | Yes | N/A | N/A |
| - Plan parameters | Yes | Yes | N/A | N/A |
| - Geometrical displays of plan data | Yes | Yes | N/A | N/A |
| - Field information | Yes | Yes | N/A | N/A |
| - Dose distribution | Yes | Yes | N/A | N/A |
| - DVH plot | Yes | Yes | N/A | N/A |

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7. PERFORMANCE DATA

The following performance data were provided in support of the substantial equivalence determination.

Sterilization & Shelf-life Testing

MOZI TPS is a software device and is not supplied sterile because the device doesn't come in contact with the patient. MOZI TPS is a software device and does not have a Shelf Life.

Biocompatibility

MOZI TPS is a software device and does not come in contact with the patient.

Electrical safety and electromagnetic compatibility (EMC)

MOZI TPS is a software device hence, no Electromagnetic Compatibility and Electrical Safety testing was conducted for the Subject Device.

Software Verification and Validation Testing (Non-Clinical 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. Software verification and validation testing was performed according to the FDA Quality System Regulation (21 CFR Part 820), ISO 13485 Quality Management System standard, IEC 62304 Software Life Cycle standard, and ISO 14971 Risk Management Standard.

Commissioning test of Intensity-modulated radiation treatment planning system

The commissioning test was performed to verify the IMRT/VMAT performance of the subject device according to AAPM TG-119 Report. The Monte Carlo dose computation algorithm in subject device has been successfully validated for accuracy in clinically relevant settings according to specification.

Radiotherapy Treatment Planning System Software performance test

The performance test was performed on subject device and predicate device (K172163) to evaluate the performance in formulating external irradiation photon therapy plan. The subject device and predicate device both support Dose Calculation feature, the subject device incorporates a Monte Carlo based algorithm while the predicate device an AAA/AXB based algorithm. However, Monte Carlo、AAA、AXB are all universally acknowledged algorithm on dose calculation. And the algorithm used in subject device has been verified for its performance comparing against that of the predicate device, the result demonstrated that they have equivalent performance.

Deformable registration performance test

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The automatic deformable registration algorithm performance test was performed against the reference device (K182624) to evaluate the deformable registration accuracy. All fixed images and moving images were generated in healthcare institutions in U.S. The scanner models covered five major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. The Normalized Mutual Information (NMI) and Hausdorff Distance (HD) were used for evaluation. NMI and HD values were calculated on two sets of images for both the proposed device and predicate device (K182624). respectively. The NMI and HD values of the subject device was compared with that of the predicate device. According to the results, it could be concluded that the NMI and HD values of the proposed device was non-inferiority compares with that of the predicate device.

Standards conformance test

A verification test was performed on subject device to verify the compliance of IEC 62083-2009 Standard. It is verified that the subject device has met the requirements for the safety of radiotherapy treatment planning systems.

End-to-end test

We conducted end-to-end test for 18 patients' treatment planning including the Simulation CT images import to the TPS, details of the contouring and image registration, step by step processes of the treatment planning and the optimization for IMRT and VMAT treatments, dose calculations, exporting the treatment plans to the LINAC software for delivery, and validation of the TPS with phantom measurement. The treatment planning in the test contains the cases with lung, liver, brain, and Head & Neck cancers using the IMRT and VMAT techniques. The test results are all passed that the entire workflow for the treatment planning have been adequately validated prior to the treatment of patients.

Structure delineation performance test

Mean Dice Similarity Coefficient (DSC) was used to evaluate the performance of Segmentation Function. The segmentation function of subject device has been verified for its performance comparing against that of the reference device K191928, by the DSC assessment method. The result demonstrated that they have equivalent performance.

The DSC values of the subject device' s auto segmentation on the testing image set with 38 sample size are as follows:

Body Part	OAR	Mean DSC values	Mean standard deviation
		Head&Neck	Brain
Brainstem	0.88		0.03
BrachialPlexus_L	0.61		0.05
BrachialPlexus_R	0.64		0.05
Esophagus	0.84		0.02
Eye-L	0.93		0.02
k) Summary
	Eye-R	0.93	0.02
	InnerEar-L	0.78	0.06
	InnerEar-R	0.82	0.04
	Larynx	0.87	0.02
	Lens-L	0.77	0.07
	Lens-R	0.72	0.08
	Mandible	0.90	0.02
	MiddleEar_L	0.73	0.04
	MiddleEar_R	0.74	0.04
	OpticNerve_L	0.61	0.07
	OpticNerve_R	0.62	0.08
	OralCavity	0.90	0.03
	OpticChiasm	0.64	0.10
	Parotid-L	0.83	0.03
	Parotid-R	0.83	0.04
	PharyngealConstrictors_U	0.87	0.03
	PharyngealConstrictors_M	0.88	0.02
	PharyngealConstrictors_L	0.87	0.03
	Pituitary	0.74	0.14
	SpinalCord	0.85	0.04
	Submandibular_L	0.86	0.04
	Submandibular_R	0.87	0.03
	TemporalLobe_L	0.89	0.03
	TemporalLobe_R	0.89	0.03
	Thyroid	0.86	0.03
	TMJ_L	0.79	0.06
	TMJ_R	0.74	0.06
	Trachea	0.90	0.02
	Esophagus	0.80	0.05
Thorax	Heart	0.98	0.01
	Lung_L	0.99	0.00
	Lung_R	0.99	0.00
	Spinal Cord	0.97	0.02
	Trachea	0.95	0.02
Abdomen	Duodenum	0.64	0.05
	Kidney_L	0.96	0.02
	Kidney_R	0.97	0.01
	Liver	0.95	0.02
	Pancreas	0.79	0.04
	SpinalCord	0.82	0.02
	Stomach	0.89	0.02
Pelvic-Man	Bladder	0.92	0.03
	BowelBag	0.89	0.04

	FemurHead_L	0.96	0.02
	FemurHead_R	0.95	0.02
	Marrow	0.90	0.02
	Prostate	0.85	0.04
	Rectum	0.88	0.03
	SeminalVesicle	0.72	0.07
Pelvic-Female	Bladder	0.88	0.02
	BowelBag	0.87	0.02
	FemurHead_L	0.96	0.02
	FemurHead_R	0.95	0.02
	Marrow	0.89	0.02
	Rectum	0.77	0.04

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For CT structure models there were 560 training and 187 testing image sets. The training image set source is from China, and the testing image source is from the United States. They are independent of each other.

The test data set information is as follows:

(1) Among the patients used for CT testing 57% were male and 43% female. Patient ages range 21-30 : 0.3%, 31-50 : 31%, 51-70 : 51.3%, 71-100 : 14.4%. Race 78% White, 12% Black or African American, 10% Other.
(2) CT datasets spanned across treatment subgroups most typically found in a radiation therapy treatment clinic with the most common diagnosis being cancers of the Head and Neck (20.3%), Esophageal and Lung (Thorax, 20.3%), Gastrointestinal (Abdomen, 20.3%), Prostate (Male Pelvis, 20.3%), Other (Female Pelvis, 18.7%).
(3) The images were obtained using scanners supplied by GE/Philips/Siemens, including 28.3% by GE, 33.7% by Philips and 38% by Siemens. And the images contained different slice thicknesses, distributed as follows: 5.3% 1mm, 28.3% 2mm, 2.7% 2.5mm, 23% 3mm, 40.6% 5mm slice thickness.

Ground truthing of test data set was generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists.

Cybersecurity Study

The cybersecurity of the subject device has been comprehensively risk assessed and tested, and has traceability. Corresponding supporting documents are provided in this submission.

Mechanical and Acoustic Testing

Not Applicable (Standalone Software).

Animal Study

No animal studies were conducted using the Subject Device, MOZI TPS.

Human Clinical Performance Testing

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Clinical trials were not performed as part of the development of this product. Clinical testing on patients is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk.

8. CONCLUSION

Based on this Discussion and Testing and Performance Data, the subject device is determined to be as safe and effective as its predicate device Eclipse TPS (K172163), and performs as well in automatic rigid registration and structure delineation as the reference device 1, AccuContour™ (K191928), the subject device also performs as well in automatic deformable registration as the reference device 2, MIM-MRT Dosimetry (K182624).