QOCA® image Smart RT Contouring System is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.

Contours that are generated by QOCA® image Smart RT Contouring System may be used as input for clinical workflows including external beam radiation therapy treatment planning. QOCA® image Smart RT Contouring System must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by OOCA® image Smart RT Contouring System. The output of QOCA® image Smart RT Contouring System in the format of RTSTRUCT objects are intended to be used by radiation oncology department.

QOCA® image Smart RT Contouring System does not provide a user interface for data visualization. System settings, user settings, progress status, and other functionalities are managed via a web-based interface.

The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.

Device Description

QOCA® image Smart RT Contouring System is a post-processing software used to automatically contour DICOM CT imaging data using deep-learning-based algorithms. OOCA® image Smart RT Contouring System contouring workflow supports CT inout data and produces RTSTRUCT outputs. Contours that are generated by QOCA® image Smart RT Contouring System may be used as input for clinical workflows including external beam radiation therapy treatment planning.

The output of QOCA® image Smart RT Contouring System, in the form of RTSTRUCT objects, are intended to be used by radiation oncology department. The output of QOCA® image Smart RT Contouring System must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by QOCA® image Smart RT Contouring System.

QOCA® image Smart RT Contouring System includes the following functionality:

Automated contouring of organs at risk (OAR) workflow
- Input - DICOM CT
- Output - DICOM CT (Original), DICOM RTSTRUCT
Web-based interface of system settings, user settings, and checking progress status

QOCA® image Smart RT Contouring System is intended to be used on adults undergoing treatment that requires the identification of anatomical structures in the body considered to be OAR. QOCA® image Smart RT Contouring System is intended to be used in the head, neck, and pelvis regions.

AI/ML Overview

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Body Part	OARs	Acceptance Criteria (DSC)	Model Performance (DSC ± std dev)	Model Performance (HD95 ± std dev)
Head and Neck	Brain stem	0.87	0.942 (±0.0215)	4.173 (±20.9737)
	Esophagus	0.76	0.875 (±0.0859)	4.694 (±5.5237)
	Mandible	0.93	0.956 (±0.0167)	1.413 (±0.9036)
	Pharyngeal constrictor muscle	0.70	0.820 (±0.0692)	2.232 (±1.3013)
	Spinal cord	0.87	0.931 (±0.0282)	2.330 (±3.3562)
	Thyroid	0.83	0.873 (±0.1756)	3.249 (±5.7852)
	Right eye	0.91	0.956 (±0.0149)	2.038 (±0.9599)
	Left lens	0.80	0.876 (±0.1150)	1.526 (±1.0436)
	Left optic nerve	0.66	0.805 (±0.0849)	3.548 (±3.0927)
	Right parotid	0.86	0.924 (±0.0303)	3.825 (±2.7730)
Pelvis	Anorectum	0.70	0.929 (±0.0755)	7.929 (±14.2608)
	Bladder	0.82	0.959 (±0.0912)	4.402 (±9.7696)
	Bowel bag	0.70	0.944 (±0.0338)	11.237 (±8.5063)
	Lumbar spine L5	0.90	0.960 (±0.0648)	5.985 (±31.2018)
	Bilateral seminal vesicles	0.64	0.818 (±0.3178)	3.638 (±6.6927)
	Right iliac	0.90	0.985 (±0.0111)	10.108 (±51.8553)
	Right proximal femur	0.90	0.980 (±0.0195)	13.193 (±68.4094)

Note: The reported device performance (Model Performance) shows the Dice Similarity Coefficient (DSC) as the primary metric for acceptance criteria. Hausdorff Distance 95 (HD95) is also provided as a secondary metric for model performance, but specific acceptance criteria for HD95 are not explicitly stated in the table. The text states "The subject device achieved a median DSC > 0.80," indicating an overarching criterion as well.

Study Details

Sample Size used for the test set and the data provenance:
- Sample Size (Test Set): 220 cases (110 head and neck CT images and 110 pelvis CT images).
- Data Provenance:
  - 50 cases from Taiwan (for each anatomical site, totaling 100 cases).
  - 60 cases from United States public datasets (TCIA - The Cancer Imaging Archive) (for each anatomical site, totaling 120 cases).
- Type of Study: Retrospective performance study.
- Independence: This test set is explicitly stated to be independent of the data used for nonclinical tests (which included training, validation, and a smaller test set).
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document does not explicitly state the number of experts used to establish the ground truth for the test set, nor their specific qualifications.
- It only mentions that "Ground truth annotations were established following CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines and Pelvic Normal Tissue Contouring Guidelines for Radiation Therapy: A Radiation Therapy Oncology Group Consensus Panel Atlas." This implies that the ground truth was created by human experts adhering to well-established clinical guidelines for radiation therapy contouring.
Adjudication method for the test set:
- The document does not specify an adjudication method (e.g., 2+1, 3+1). It only mentions that ground truth was "established following" various consensus guidelines.
If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
- No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not done. The study described is a standalone performance validation of the AI algorithm.
If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Yes, a standalone performance study was done. The section title is "Segmentation Performance Test" and it states, "The standalone performance of the subject device has been validated in a retrospective performance study on CT data previously acquired for RT treatment planning."
The type of ground truth used:
- Expert Consensus/Clinical Guidelines: The ground truth annotations for the test set were established by human experts "following CT-based delineation of organs at risk" based on several recognized consensus guidelines (DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG for Head and Neck; RTOG Consensus Panel Atlas for Pelvis).
The sample size for the training set:
- Total Initial Data: 317 cases of head and neck images and 351 cases of pelvic images (total 668 cases).
- Training Set Size: These initial cases were distributed in an "8:1:1 ratio into Training datasets, Validation datasets, and Test datasets."
  - Therefore, the training set would be approximately 8/10ths of the total initial data:
    - Head and Neck training: 0.8 * 317 ≈ 254 cases
    - Pelvic training: 0.8 * 351 ≈ 281 cases
    - Total Training Set: Approximately 535 cases (254 + 281).
- Note: This training set data is distinct from the 220 cases used for the final standalone performance test.
How the ground truth for the training set was established:
- The document states that the initial data (used for training, validation, and an internal test set) was "retrospectively collected from 2000 to 2021 from two hospitals in Taiwan".
- It doesn't explicitly detail the process of ground truth establishment for the training set, but given the context of medical imaging for radiation therapy, it's highly implied that these contours were also created by clinical experts (e.g., radiation oncologists or dosimetrists) at those hospitals, likely following standard clinical practices. The subsequent "Segmentation Performance Test" details how ground truth for the final evaluation set was established ("following CT-based delineation... consensus guidelines"), suggesting a similar rigorous approach for the data used in training.

Summary

{0}------------------------------------------------

February 13, 2024

Image /page/0/Picture/1 description: The image shows 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 consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath.

Quanta Computer Inc. % Joe Wang, Research Specialist No. 188, Wenhua 2nd Rd Guishan Dist. Taoyuan City, 33383 TAIWAN

Re: K231855

Trade/Device Name: QOCA® image Smart RT Contouring System Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: January 8, 2024 Received: January 8, 2024

Dear Joe Wang:

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/cdrb/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 OS 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,

Lora 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) K231855

Device Name

QOCA® image Smart RT Contouring System

Indications for Use (Describe)

QOCA® image Smart RT Contouring System is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.

by radiation oncology department.

The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{3}------------------------------------------------

510(k) Summary

5.1. Type of Submission:	Traditional
5.2. Date of Summary:	01/08/2024
5.3. Submitter:	Quanta Computer Inc.
Address:	No. 188, Wenhua 2nd Rd., Guishan Dist. TaoyuanCity 33383, Taiwan (R.O.C)
Phone:	+886-3-327-2345
Contact:	Joe Wangjoe_wang@quantatw.com

5.4. Identification of the Device:

Proprietary/Trade Name:	QOCA® image Smart RT Contouring System
Model Number:	ZSWR901
Review Panel:	Radiology
Regulation Name:	Medical Image Management and Processing System
Regulation Number:	21 CFR 892.2050
Product Code:	QKB
Device Class:	II

5.5. Identification of the Predicate Device:

Predicate Device Name:	AccuContour™
Model Number:	--
510(k) Number:	K191928
Manufacturer:	Xiamen Manteia Technology LTD.
Regulation Number:	21 CFR 892.2050
Product Code:	QKB
Device Class:	II

{4}------------------------------------------------

5.6. Intended Use/Indications for Use of the Device

QOCA® image Smart RT Contouring System is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.

The output of QOCA® image Smart RT Contouring System in the format of RTSTRUCT objects are intended to be used by radiation oncology department.

The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.

5.7. Device Description

QOCA® image Smart RT Contouring System includes the following functionality:

Automated contouring of organs at risk (OAR) workflow ●
- Input - DICOM CT
- Output - DICOM CT (Original), DICOM RTSTRUCT

{5}------------------------------------------------

Web-based interface of system settings, user settings, and checking progress status
QOCA® image Smart RT Contouring System is intended to be used on adults undergoing treatment that requires the identification of anatomical structures in the body considered to be OAR. QOCA® image Smart RT Contouring System is intended to be used in the head, neck, and pelvis regions.

5.8. Comparison of Technological Characteristics with the Predicate Device

QOCA® image Smart RT Contouring System submitted in this 510(k) file is substantially equivalent in intended use, safety and performance to the cleared AccuContour™ (K191928). Differences between the devices cited in this section do not raise any new issue of substantial equivalence.

Item	Subject Device	Predicate Device	SubstantialEquivalenceDetermination
510(k) Number	K231855	K191928	--
ProprietaryName	QOCA® image SmartRT ContouringSystem	AccuContour™	--
Manufacturer	Quanta Computer Inc.	Xiamen ManteiaTechnology LTD.	--
RegulationNumber	21 CFR 892.2050	21 CFR 892.2050	Same
Product Code	QKB	QKB	Same
Classification	Class II	Class II	Same
IntendedUse/Indicationfor Use	QOCA® image SmartRT ContouringSystem is a post-processing softwareintended toautomatically contourDICOM CT imagingdata using deep-learning-basedalgorithms.	It is used byradiation oncologydepartment toregistermultimodalityimages andsegment (non-contrast) CTimages, to generateneeded information	SimilarBoth devices utilizeartificialintelligencealgorithms toautomaticallycontour organs atrisk, including thehead and neck, aswell as the pelvis.
Contours that are	for treatment	for radiation
generated by QOCA®	planning, treatment	treatment planning.
image Smart RT	evaluation and
Contouring System	treatment
may be used as input	adaptation.
for clinical workflows
including external
beam radiation
therapy treatment
planning. QOCA®
image Smart RT
Contouring System
must be used in
conjunction with
appropriate software
such as Treatment
Planning Systems and
Interactive Contouring
applications, to
review, edit, and
accept contours
generated by QOCA®
image Smart RT
Contouring System.
The output of QOCA®
image Smart RT
Contouring System in
the format of
RTSTRUCT objects
are intended to be
used by radiation
oncology department.
QOCA® image Smart
RT Contouring
System does not
	provide a userinterface for datavisualization. Systemsettings, user settings,progress status, andother functionalitiesare managed via aweb-based interface.The software is notintended toautomatically detect orcontour lesions. OnlyDICOM images ofadult patients areconsidered to be validinput.
Operating System	Windows	Windows	Same
Algorithm	Deep Learning	Deep Learning	Same
Segmentation ofOrgan at Risk inthe AnatomicRegions	Brain stem,Esophagus, Mandible,Pharyngeal,Constrictor Muscle(PCM), Spinal cord,Thyroid, Right eye,Light eye, Right lens,Left lens, Right opticnerve, Left opticnerve, Right parotid,Left parotid,Anorectum, Bladder,Bowel bag, Lumbarspine L5, Bilateralseminal vesicles,Right iliac, Left iliac.	Head and Neck,Thorax, Abdomen,and Pelvis	SimilarThe subject devicecontains head andneck, and pelvis.
Left proximal femur
CompatibleModality	CT Images	Non-Contrast CTImages	SimilarThe subject deviceand the predicatedevice are bothcompatible onlywith CT images forthe segmentationfeature. Thepredicate deviceclaims to handleonly non-contrastCT images, whilethe subject devicecan be used withboth contrast andnon-contrast CTimages.
CompatibleScanner Models	No specificrequirement for thescanner model, it isrecommended to usemulti-detector CT(MDCT) equipmentwith more than 16slices for RadiationTherapy SimulationCT, and DICOMcompliance required.	No Limitation onscanner model,DICOM 3.0compliancerequired.	SimilarThe Subject Deviceand PredicateDevice aresubstantiallyequivalent in theirrequirements forDICOMcompliance,ensuringinteroperability andstandardization inmedical imagingdata. The SubjectDevicerecommends a
			scanner with more
			than 16 slices
			specifically for
			Radiation Therapy
			Simulation CT to
			provide detailed
			imaging necessary
			for accurate therapy
			planning.
CompatibleTreatmentPlanning System	No Limitation on TPSmodel, DICOMcompliance required.	No Limitation onTPS model,DICOM3.0compliancerequired.	Same
	Contraindications	Adult use only.	There are noknown specificsituations thatcontraindicate theuse of this device.	SimilarThe subject deviceis designed foradult use, and thereare no knownspecific situationsthat contraindicateits usage.
	SegmentationPerformance	The segmentationperformance wasvalidated using privatedatasets from Taiwanand public datasetswas collected fromvarious sources,including the USA.The datasets wereobtained from majorvendors such as GE,Siemens, Philips, andTOSHIBA. The	The segmentationperformance wasvalidated usingdatasets fromChina and the USAusing three majorvendors (GE,Siemens andPhilips). Thesegmentationaccuracy isevaluated usingDICE similarity	Same

{6}------------------------------------------------

{7}------------------------------------------------

K231855

{8}------------------------------------------------

{9}------------------------------------------------

{10}------------------------------------------------

accuracy of thesegmentation wasevaluated using theDICE similaritycoefficient (DSC).	coefficient (DSC).
-----------------------------------------------------------------------------------------------------	--------------------

Similarity and Difference

The subject device has the similar intended use and features to the predicate device. Both devices are intended to aid user to contour the organ-at-risk (OAR) by artificial intelligence algorithm. And they are not intended to be used on a stand-alone basis for clinical decisionmaking or clinical diagnosis.

The primary difference between the subject and predicate devices lies in their "Compatibility with Scanner Models" and "Imaging Modalities". However, the subject device's approach to presenting results aligns with that of the predicate device. Furthermore, both devices meet the requirements for DICOM compliance, ensuring interoperability and standardization in medical imaging data. Notably, the subject device recommends the use of a multi-detector CT scanner with more than 16 slices for radiation therapy simulation CT, enabling detailed imaging essential for precise therapy planning. Both devices exclusively support CT images for the segmentation feature. While the predicate device is limited to non-contrast CT images, the subject device can handle both contrast and noncontrast CT images. Therefore, it will not affect the substantial equivalence.

5.9. Performance Data

The subject device, QOCA® image Smart RT Contouring System has been evaluated and verified in accordance with software specifications and applicable performance standards to ensure performance.

The subject device has undergone software validation activities in accordance with IEC 62304: 2006/A1:2016 - Medical device software - Software life cycle processes, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, Content of Premarket submissions for Devices Software Functions and Content of Premarket Submission for Management of Cybersecurity in Medical Devices.

The performance of the subject device has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification

{11}------------------------------------------------

and validation testing.

Nonclinical Tests

During the development of the model, CT images had retrospectively collected from 2000 to 2021 from two hospitals in Taiwan, comprising a total of 317 cases of head and neck images and 351 cases of pelvic images. These images were distributed in an 8:1:1 ratio into Training datasets, Validation datasets, and Test datasets.

In the Test datasets, an additional 38 cases from United States public datasets (The Cancer Imaging Archive (TCIA), and Gold Atlas - Male Pelvis (GA)) were included to ensure that the intended patient population in the United States could achieve the expected purpose.

The CT images come from the following equipment:

GE Discovery CT590 RT ●
GE Discovery STE
GE Discovery ST
SIEMENS SOMATOM PLUS 4
SIEMENS Sensation 64
PHILIPS Brilliance Big Bore
TOSHIBA Aquilion ONE
TOSHIBA Aquilion/LB

Segmentation Performance Test

The standalone performance of the subject device has been validated in a retrospective performance study on CT data previously acquired for RT treatment planning.

Data collection included 110 head and neck CT images and 110 pelvis CT images, with each anatomical site contributing 50 cases from Taiwan and 60 cases from the United States public dataset, TCIA. These 220 cases are independent of the data used in nonclinical tests and have not been reused. The demographic distribution of this data is as follows:

Gender: 97 Female (55 Head and Neck; 42 Pelvis), 123 Male (55 Head and Neck; 68 Pelvis).
Age: Adult (above 22 years old).
Ethnicity: Data collected from the United States and Taiwan.
Equipment:
- GE Discovery ST .

{12}------------------------------------------------

GE MEDICAL SYSTEMS LightSpeed Plus .
SIEMENS Sensation Open .
PHILIPS Brilliance Big Bore .
. PHILIPS GEMINI TF Big Bore
. TOSHIBA Aquilion ONE

Ground truth annotations were established following CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines and Pelvic Normal Tissue Contouring Guidelines for Radiation Therapy: A Radiation Therapy Oncology Group Consensus Panel Atlas.

Body Part	OARs	Acceptance Criteria(DSC)	Model Performance
Head andNeck	Brain stem	0.87	0.942(±0.0215)	4.173(±20.9737)
	Esophagus	0.76	0.875(±0.0859)	4.694(±5.5237)
	Mandible	0.93	0.956(±0.0167)	1.413(±0.9036)
	Pharyngealconstrictor muscle	0.70	0.820(±0.0692)	2.232(±1.3013)
	Spinal cord	0.87	0.931(±0.0282)	2.330(±3.3562)
	Thyroid	0.83	0.873(±0.1756)	3.249(±5.7852)
	Right eye	0.91	0.956(±0.0149)	2.038(±0.9599)
	Left lens	0.80	0.876(±0.1150)	1.526(±1.0436)
	Left optic nerve	0.66	0.805(±0.0849)	3.548(±3.0927)
	Right parotid	0.86	0.924(±0.0303)	3.825(±2.7730)

The results for standalone performance testing are as follows:

{13}------------------------------------------------

Body Part	OARs	Acceptance Criteria(DSC)	Model Performance
			DSC	HD95
Pelvis	Anorectum	0.70	0.929(±0.0755)	7.929(±14.2608)
	Bladder	0.82	0.959(±0.0912)	4.402(±9.7696)
	Bowel bag	0.70	0.944(±0.0338)	11.237(±8.5063)
	Lumbar spine L5	0.90	0.960(±0.0648)	5.985(±31.2018)
	Bilateral seminalvesicles	0.64	0.818(±0.3178)	3.638(±6.6927)
	Right iliac	0.90	0.985(±0.0111)	10.108(±51.8553)
	Right proximalfemur	0.90	0.980(±0.0195)	13.193(±68.4094)

The subject device achieved a median DSC > 0.80. Furthermore, organs not meeting predefined acceptance criteria were excluded from the product's intended use to ensure reliability and accuracy. In the performance results, it was found that regardless of whether contrast was injected or not, the system achieved the expected performance outcomes.

5.10.Clinical Tests

No clinical test was conducted as part of submission to prove substantial equivalence.

5.11.Conclusion

The subject device has a similar intended use to the predicate device, and the slight difference does not affect the substantial equivalence. In addition, there are no differences in technological characteristics that affect the safety and effectiveness of the subject device relative to the predicate. Moreover, the performance testing results are similar to the predicate device. Therefore, the subject device, QOCA® image Smart RT Contouring System, is substantially equivalent to the predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

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