TRAQinform IQ is a software only device that provides a quantitative TRAQinform Report on lesions identified as Regions of Interest (ROI) in PET/CT DICOM compliant imaging data acquired, interpreted, and reported on per local practice prior to device use.

Clinicians responsible for patient care and for ordering TRAQinform Reports as an adjunct to locally reported image interpretation do not interact directly with the device. Clinicians responsible for local image interpretation do not interact with the device and generate their reporting before and independently of the TRAQinform Report.

The TRAQinform Report is generated by the device manufacturer and signed by a U.S. board certified physician responsible for supervising central report generation and qualified to practice nuclear radiology/medicine. The TRAQinform Report is for use by trained medical professionals including but not limited to oncologists, nuclear radiologists/physicians, medical imaging technologists, dosimetrists, and physicists.

TRAQinform IQ software contains the following functionalities:

Automated matching of ROI between previously performed CT and PET/CT DICOM 3.0 volumetric medical images.
In order to perform automated matching of ROI and quantitative analysis of previously performed CT and PET/CT DICOM 3.0 volumetric medical images, the software initially performs the following functions:
Machine learning skeletal and anatomic structure segmentation.
Threshold-based ROI identification and contouring.
Automated quantitative analysis to assess previously performed CT and PET/CT DICOM 3.0 volumetric medical images, including: change in total volume and density of each identified ROI, and change in Fludeoxyglucose F18 (FDG) tracer uptake of each identified ROI among images.
Generation of images of the anatomy combined with spatial and quantitative information, including computed classification of quantitative FDG ROI changes.

For multi-timepoint quantitative analysis, recommended use is in adult patients 22 years and older with partial or whole-body PET/CT acquired following administration of FDG per approved drug prescribing information and with the second FDG administration separated from the first by a period not to exceed 12 months.

For single-timepoint quantitative analysis, recommended use is in adult patients 22 years and older with partial or whole-body PET/CT following administration of FDG, a PSMA targeted PET drug, or a SSTR-targeted PET drug per approved drug prescribing information.

Discrepancies between TRAQinform IQ and local PET/CT reporting have been investigated and use of TRAQinform IQ has not been established for binary patient level progression or non-progression decisions without multidisciplinary review. Discrepancies between TRAQinform IQ and local PET/CT reporting that could impact patient care should therefore prompt consultation with subject matter experts (for example, in tumor board), with a patientcentered focus on discrepant imaging regions and with blinded or otherwise neutral adjudication regarding interpretation/classification source.

TRAQinform IQ is not intended to diagnose any disease, replace the diagnostic procedures for interpretation of CT or PET/CT images, recommend any specific treatment, nor is it intended to replace the skill and judgment of a qualified medical professional.

Device Description

TRAQinform IQ is a software only device that provides quantitative analysis of lesions identified as Regions of Interest (ROI) in PET/CT DICOM compliant imaging data acquired, interpreted, and reported on per local practice prior to device use.

The input to TRAQinform IQ is CT and PET/CT images as supported by ACR/NEMA DICOM 3.0.

The following steps are performed by the software:

Automatic threshold-based ROI segmentation:
ROI can also be imported from external sources (other validated tools or manual contouring by qualified medical personnel).
Automatic ROI registration between multiple images:
Images can be from the same or different imaging modality.
Images can be from the same or different PET tracer.
Images can be from the same or different date.
Automatic matching of ROI between multiple, previously performed images.
Automatic quantification of dynamic changes among images including, but not limited to:
Changes in ROI shape.
Single ROI splitting into multiple ROI.
Multiple ROI combining into a single ROI.
ROI appearing, disappearing, and re-appearing across images.
A comprehensive summary analysis.

TRAQinform IQ calculates spatial and quantitative metrics for each individual ROI. These metrics are provided as a TRAQinform Report. TRAQinform IQ uses computational algorithms to detect, fuse and analyze ROI and provides the following outputs:

Identification of anatomic location of ROI in all areas of the body.
A quantitative analysis of functional and anatomic data for CT and PET/CT scans, including:
Volume of all identified ROI on each image;
Change in volume of each identified ROI among images:
Total volume of all identified ROI on each image;
Change in total volume of all identified ROI on each image;
Heterogeneity of change in volume of each identified ROI:
For PET scans:
Tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of each identified ROI on each image:
Change in tracer uptake (SUVmax, SUVtotal, SUVmean) of each identified ROI among images:
Total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image:
Change in total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image:
Heterogeneity of change in tracer uptake (SUVhetero) among identified ROI.
For CT scans
Radio density (HUmax, HUtotal, HUmean, HUhetero) for each identified ROI on each image:
Changes in radio density (HUmax. HUtotal. HUmean) of all identified ROI on each image:
Change in total radio density (HUmax, HUtotal, HUmean, HUhetero) of all identified ROI on each image;
Heterogeneity of change in radio density (HUhetero) among identified ROI.
2D graphical renderings of medical images, including Maximum Intensity Projections of the PET and CT, with overlayed and labeled/color-coded ROI, for inclusion in TRAQinform Reports.
3D labeled contours for ROI, anatomic structures, and skeletal structures.

The TRAQinform IQ software operates in a secure cloud environment.

AI/ML Overview

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

Acceptance Criteria and Device Performance Study for TRAQinform IQ

1. Acceptance Criteria and Reported Device Performance

The provided text summarizes performance data from two studies: a "Test-Retest" reliability study and a "Pivotal Reader Study." The acceptance criteria, while not explicitly stated as "acceptance criteria" for regulatory submission, can be inferred from the reported performance measures and context of the studies.

Test-Retest Study: Limits of Repeatability for Quantitative Features

This study established the expected variability of the device's quantitative measurements. The limits of repeatability serve as an informal "acceptance criteria" for the intrinsic variability of the measurements.

Feature	Lower Limit (%)	Upper Limit (%)
SUVmax	-27.0	56.8
SUVmean	-20.2	38.5
SUVtotal	-54.1	144.5
Volume	-52.6	113.9

Reported Device Performance: The table above is the reported device performance for the test-retest study, indicating the interval within which 95% of repeated measurements are expected to lie.

Pivotal Reader Study: Agreement with Expert Panel

This study evaluated the clinical utility of TRAQinform IQ by assessing how well its output, when presented to oncologists, aligned with an expert panel's assessment. The "acceptance criteria" here would be an adequate level of agreement, though specific thresholds are not explicitly defined as pass/fail.

Metric	Reported Device Performance
Overall Percent Agreement (OPA) with panel (95% CI)	41% to 76% (compared to chance performance of 50%)
Positive Percent Agreement (PPA) (oncologists vs. panel for positive progression)	14/18 = 78%
Negative Percent Agreement (NPA) (oncologists vs. panel for negative progression)	5/14 = 36%
Agreement between TRAQinform IQ classification and panel for highlighted ROIs (by ROI classification)	New: 37/53 (70%)Increasing: 18/26 (70%)Unchanged: 11/13 (85%)Decreasing: 5/8 (63%)Disappearing: 10/11 (90%)

Note on Acceptance: The document states that the "performance data demonstrate that the TRAQinform IQ is as safe and effective as the QTxl." This implies that the reported performance metrics were deemed acceptable for substantial equivalence.

2. Sample Sizes and Data Provenance

Test-Retest Study:

Sample Size: 31 patients.
Data Provenance: Patients with non-small cell lung cancer, received two FDG PET/CT images within 1 week pretreatment. No explicit location (e.g., country) is given, but the context of an FDA submission suggests data highly relevant to the US market. The study design (two scans within 1 week) indicates a prospective, controlled data collection for evaluating reliability.

Pivotal Reader Study:

Sample Size: 103 patients, each with two sequential FDG PET/CT scans (total 206 scans).
Data Provenance: Images acquired between 2005 and 2022 from patients scanned at 10 or more imaging centers in at least 3 U.S. states. This indicates retrospective data collection from real-world clinical practice in the USA. Specific scanner information (manufacturers and models) is provided, and 84 patients had scans on the same scanner for baseline and follow-up. Patient demographics (cancer type, sex, age, weight, race) are also detailed.

3. Number of Experts and Qualifications for Ground Truth

Test-Retest Study:

The ground truth for this study was based on the device's own measurements. There is no mention of expert image interpretation being used to establish a ground truth for "limits of repeatability."

Pivotal Reader Study:

Number of Experts: A panel of three experts was used.
Qualifications of Experts: Two radiologists and one oncologist. No specific experience levels (e.g., "10 years of experience") are explicitly given, but their titles (radiologist, oncologist) imply qualified medical professionals in their respective specialties.

4. Adjudication Method for the Test Set

Pivotal Reader Study (for expert panel ground truth):

The data states: "Imaging and local reporting on these 23 + 9 = 32 patients was sent to a panel of two radiologists and one oncologist, together serving as a reference source against which to quantify..." This suggests a consensus-based adjudication method (all three experts together formed the reference source), rather than a majority rule or other multi-reader approach. The document doesn't specify if it was a "2+1" or "3+1" approach, but it implies a collective decision by the panel.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a form of MRMC study was done. The "Pivotal Reader Study" involved three oncologist "report evaluators" reading cases without and then with the adjunctive TRAQinform Report.
Effect Size of Human Reader Improvement: The study demonstrates how the AI assistance changes human reader interpretations.
- 23 patients initially classified as "negative for progression" by oncologists without the device were reevaluated to "positive" with the device.
- 9 patients initially classified as "positive for progression" by oncologists without the device were reevaluated to "negative" with the device.
This indicates that the AI report prompted a re-evaluation and change in classification for 32 out of 103 patients (approx. 31%). The "effect size" is the shift in clinical decision, influencing a significant percentage of cases. The PPA and NPA against the expert panel further quantify the agreement (or disagreement) after AI assistance. The key finding is the change in oncologist assessment, even if not directly framed as an "improvement" in accuracy by the text itself, but rather as influencing the decision toward what the expert panel considered ground truth.

6. Standalone (Algorithm Only) Performance

Yes, standalone performance aspects were evaluated indirectly.
- The "Test-Retest" study primarily assesses the standalone stability and reproducibility of the algorithm's quantitative measurements (SUVmax, SUVmean, SUVtotal, Volume) without human interaction influencing the values themselves.
- The "agreement... with the device classification" for ROIs highlighted by the report evaluators (70-90% for various ROI changes) is also a measure of the algorithm's performance against the expert panel's assessment. This isn't a full "standalone diagnostic accuracy" but rather an evaluation of the algorithm's output (classification of ROI changes) compared to the panel.
- The text also references "early feasibility testing of device component functionality," with published reporting available for CT anatomy segmentation (Weisman 2023), ROI detection methodology (Perk 2018), and ROI matching (Huff 2023). While not detailed here, these studies would likely have included standalone performance evaluation for those specific algorithmic components.

7. Type of Ground Truth Used

Expert Consensus: For the "Pivotal Reader Study," the primary ground truth for patient-level progression assessment (PPA, NPA, OPA) was established by a panel of two radiologists and one oncologist acting as a "reference source."
Inherent Ground Truth (device's own outputs): For the "Test-Retest" study, the ground truth for repeatability was the device's own measurements across repeated scans, assessed for consistency.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set used for the TRAQinform IQ algorithm.
It does mention that the software uses "Machine learning skeletal and anatomic structure segmentation" (page 4, also page 7, 8). While the exact training dataset size isn't listed, the reference to published papers (Weisman 2023, Perk 2018, Huff 2023) suggests that the underlying machine learning components would have been trained using relevant datasets as described in those publications.

9. How Ground Truth for Training Set was Established

The document does not explicitly describe how the ground truth for the training set was established.
Given the mention of "Machine learning skeletal and anatomic structure segmentation" and "Threshold-based ROI identification and contouring," the ground truth for training these models would typically involve expert annotations of anatomical structures and ROIs on medical images. This would be consistent with standard practices for training medical image segmentation and detection algorithms. The referenced external publications (Weisman 2023, Perk 2018, Huff 2023) would contain details on their specific training methodologies and ground truth establishment.

Summary

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Image /page/0/Picture/0 description: The image contains the logos of the U.S. Department of Health and Human Services and the U.S. Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. Food & Drug Administration" in blue.

December 5, 2024

AIQ Global, Inc. % Kelliann Payne Partner Hogan Lovells US LLP 1735 Market Street Floor 23 Philadelphia, Pennsylvania 19103

Re: K233998

Trade/Device Name: TRAQinform IQ Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ

Dear Kelliann Payne:

The Food and Drug Administration (FDA) is sending this letter to notify you of an administrative change related to your previous substantial equivalence (SE) determination letter dated September 5, 2024. Specifically, FDA is updating this SE Letter to address a typo in the IFU form as an administrative correction.

Please note that the 510(k) submission was not re-reviewed. For questions regarding this letter please contact Daniel Krainak, OHT8: Office of Radiological Health, 301-796-0478, daniel.krainak@fda.hhs.gov.

Sincerely.

D. R. K.

Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

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September 5, 2024

AIQ Global, Inc. % Kelliann Payne Partner Hogan Lovells US LLP 1735 Market Street Floor 23 Philadelphia, Pennsylvania 19103

Re: K233998

Trade/Device Name: TRAQinform IQ Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ Dated: December 18, 2023 Received: December 18, 2023

Dear Kelliann Payne:

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.

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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 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 (OS) 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.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

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 mediation-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

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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,

D. G. K.

Enclosure

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Indications for Use

510(k) Number (if known) K233998

Device Name

TRAQinform IQ

Indications for Use (Describe)

TRAQinform IQ is a software only device that provides a quantitative TRAOinform Report on lesions identified as Regions of Interest (ROI) in PET/CT DICOM compliant imaging data acquired, interpreted, and reported on per local practice prior to device use.

TRAQinform IQ software contains the following functionalities:

· Automated matching of ROI between previously performed CT and PET/CT DICOM 3.0 volumetric medical images.
· In order to perform automated matching of ROI and quantitative analysis of previously performed CT and PET/CT DICOM 3.0 volumetric medical images, the software initially performs the following functions:
- · Machine learning skeletal and anatomic structure segmentation.
- · Threshold-based ROI identification and contouring.
· Automated quantitative analysis to assess previously performed CT and PET/CT DICOM 3.0 volumetric medical images, including: change in total volume and dentified ROI, and change in Fludeoxygluose F18 (FDG) tracer uptake of each identified ROI among images.
· Generation of images of the anatomy combined with spatial and quantitative information, including computed classification of quantitative FDG ROI changes.

For multi-timepoint quantitative analysis, recommended use is in adult patients 22 years and older with partial or wholebody PET/CT acquired following administration of FDG per approved drug prescribing information and with the second FDG administration separated from the first by a period not to exceed 12 months.

For single-timepoint quantitative analysis, recommended use is in adult patients 22 years and older with partial or wholebody PET/CT following administration of FDG, a PSMA targeted PET drug, or a SSTR-targeted PET drug per approved drug prescribing information.

Discrepancies between TRAQinform IQ and local PET/CT reporting have been investigated and use of TRAQinform IQ has not been established for binary patient level progression decisions without multidisciplinary review. Discrepancies between TRAQinform IQ and local PET/CT reporting that could impact patient care should therefore prompt consultation with subject matter experts (for example, with a patient-centered focus on discrepant imaging regions and with blinded or otherwise neutral adjudication regarding interpretation/classification source.

TRAQinform IQ is not intended to diagnose any disease. replace the diagnostic procedures for interpretation of CT or PET/CT images, recommend any specific treatment, nor is it intended to replace the skill and judgment of a qualified medical professional.

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

X Prescription Use (Part 21 CFR 801 Subpart D)

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K233998

510(k) SUMMARY AIQ Global, Inc's TRAQinform IQ

Submitter

AIQ Global, Inc. (dba AIQ Solutions) 8000 Excelsior Dr. Suite 400 Madison, WI 53717 (608) 268-9684

Contact Person

Alberti Dona, COO (608) 268-9684 dona.alberti@aiq-solutions.com

Date Prepared

Auqust 28, 2024

Device Identification

Proprietary Name: TRAQinform IQ Common Name: System, Image Processing, Radiological Regulation No: 21 CFR 892.2050 Picture Archiving and Communications System Product Code: LLZ Regulatory Class: Class II Classification Panel: Radiology

Predicate Device

Name of Device: QTxI 510(k) Number: K173444 Regulation No: 21 CFR 892.2050 Product Code: LLZ Regulatory Class: Class II Classification Panel: Radiology

Reference Device

Name of Device: MIM 4.1 (SEASTAR) 510(k) Number: K071964 Regulation No: 21 CFR 892.2050 Product Code: LLZ Regulatory Class: Class II Classification Panel: Radiology

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Device Description and Technological Characteristics

The input to TRAQinform IQ is CT and PET/CT images as supported by ACR/NEMA DICOM 3.0.

The following steps are performed by the software:

. Automatic threshold-based ROI segmentation:
- ROI can also be imported from external sources (other validated tools or manual contouring о by qualified medical personnel).
Automatic ROI registration between multiple images:
- Images can be from the same or different imaging modality. O
- Images can be from the same or different PET tracer. O
- Images can be from the same or different date. O
Automatic matching of ROI between multiple, previously performed images. ●
Automatic quantification of dynamic changes among images including, but not limited to: ●
- Changes in ROI shape. o
- Single ROI splitting into multiple ROI. O
- Multiple ROI combining into a single ROI. O
- ROI appearing, disappearing, and re-appearing across images. O
A comprehensive summary analysis. ●

. Identification of anatomic location of ROI in all areas of the body.
. A quantitative analysis of functional and anatomic data for CT and PET/CT scans, including:
- Volume of all identified ROI on each image; O
- Change in volume of each identified ROI among images: O
- Total volume of all identified ROI on each image; O
- Change in total volume of all identified ROI on each image; O
- Heterogeneity of change in volume of each identified ROI: o
- For PET scans: O
  - Tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of each identified ROI on . each image:
  - . Change in tracer uptake (SUVmax, SUVtotal, SUVmean) of each identified ROI among images:
  - . Total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image:
  - I Change in total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image:
  - . Heterogeneity of change in tracer uptake (SUVhetero) among identified ROI.
- For CT scans O
  - Radio density (HUmax, HUtotal, HUmean, HUhetero) for each identified ROI on each '

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image:

Changes in radio density (HUmax. HUtotal. HUmean) of all identified ROI on each image:
. Change in total radio density (HUmax, HUtotal, HUmean, HUhetero) of all identified ROI on each image;
트 Heterogeneity of change in radio density (HUhetero) among identified ROI.
. 2D graphical renderings of medical images, including Maximum Intensity Projections of the PET and CT, with overlayed and labeled/color-coded ROI, for inclusion in TRAQinform Reports.
. 3D labeled contours for ROI, anatomic structures, and skeletal structures.

The TRAQinform IQ software operates in a secure cloud environment.

Intended Use / Indications for Use

TRAQinform IQ software contains the following functionalities:

Automated matching of ROI between previously performed CT and PET/CT DICOM 3.0 . volumetric medical images.
. In order to perform automated matching of ROI and quantitative analysis of previously performed CT and PET/CT DICOM 3.0 volumetric medical images, the software initially performs the following functions:
- Machine learning skeletal and anatomic structure segmentation.
  - Threshold-based ROI identification and contouring.
. Automated quantitative analysis to assess previously performed CT and PET/CT DICOM 3.0 volumetric medical images, including: change in total volume and density of each identified ROI, and change in Fludeoxyglucose F18 (FDG) tracer uptake of each identified ROI among images.
. Generation of images of the anatomy combined with spatial and quantitative information. including computed classification of quantitative FDG ROI changes.

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Performance Testing

Results of software validation and verification (V&V) and early feasibility testing of device component functionality for CT segmentation, regional PET thresholding, and ROI matching supported further FDG test-retest and pivotal device output investigation. For detail on component testing, published reporting is available respectively in Weisman 2023 (CT anatomy segmentation, doi.org/10.1088/2057-1976/acfb06), Perk 2018 (ROI detection methodology, doi.org/10.1088/1361-6560/aaebba), and Huff 2023 (ROI matching, doi.org/10.1088/1361-6560/acef8f).

Test-Retest

This study established test-retest limits of agreement used to categorize regions of interest based off changes between FDG PET/CT images. 31 patients with non-small lung cancer patients received two FDG PET/CT images within 1 week pretreatment. The limits of repeatability endpoint estimated the interval within which 95% of repeated measurements are expected to lie when clinically relevant change is lacking. For TRAQinform IQ generated features, these limits are shown in Table 1.

Feature	Lower limit (%)	Upper limit (%)
SUVmax	-27.0	56.8
SUVmean	-20.2	38.5
SUVtotal	-54.1	144.5
Volume	-52.6	113.9

Table 1: Lower and upper limits of repeatability for each imaging feature.

Pivotal Reader Study

A study was conducted to evaluate the clinical utility of TRAQinform IQ. In this study, three report evaluators (oncologists) were assigned to distinguish each of 103 patients as positive versus

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neqative for progression based on presentation of the locally generated radiology report without and then with adjunctive presentation of the TRAQinform Report.

Each of the 103 patients had two sequential FDG PET/CT scans acquired between 2005 and 2022. Scans were acquired from patients scanned at 10 or more imaging centers in at least 3 U.S. states (exact image center information was redacted on scan transfer). A full breakdown is shown in Table 2. A breakdown of patients by disease type, patient sex, weight, and age are also shown in Table 2. Out of the 206 total scans (2 per patient), scanner information was available for 199 (99 patients). Scans were acquired on a variety of scanner manufacturers and models, with 84 patients receiving scans on the same scanner for baseline and follow-up.

A total of 1046 ROI were detected across the baseline scans (median per patient: 6, range: 0-80) and 935 were detected on the follow-up (median per patient: 4, range: 0-60).

Table 2: Patient and scan characteristics for all patients and scans where information was not redacted during the scan transfer process.

Imaging Site /Location	Site 1 (North Carolina, USA), n = 35Site 2 (Unknown State, USA) , n = 31 Site 3(North Carolina, USA), n = 13Site 4 (North Carolina, USA), n = 12Site 5 (Iowa, USA), n = 4Site 6 (North Carolina, USA), n = 3Site 7 (North Carolina, USA), n = 2Site 8 (Unknown State, USA), n = 1Site 9 (North Carolina, USA), n = 1Site 10 (Missouri, USA), n = 1
Cancer Type, n	Breast Cancer, n = 23Lung Cancer, n = 20Prostate, n = 16Melanoma, n = 15Head & Neck Cancer, n = 8Lymphoma, n = 7Colorectal Cancer, n = 6Other, n = 5Gynecological Cancer, n = 3
Patient sex, nFemale / Male	57/46
Patient age, yearsMedian (range)	66 (26, 87)
Patient weight, kgMedian (range)	78.5 (43.0, 132.9)
Patient Race	Unreported, n = 75White, n = 25Hispanic, n = 2Black, n = 1

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Scanner model(n=199)	Siemens Healthineers Biograph 20, n = 76Siemens Healthineers BioGraph TruePoint, n = 46Canon Medical Systems Celesteion, n = 41Canon Medical Systems Cartesion Prime, n = 14GE Healthcare Discovery ST, n = 12Siemens Healthineers Biograph6, n = 7Siemens Healthineers Biograph Horizon, n = 3
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Of the 103 patients investigated, the TRAQInform Report prompted at least one oncologist interpreter to reevaluate 23 patients classified without the device as negative for progression to positive, and to reevaluate 9 patients classified without the device as positive for progression to negative. In these cases, the oncologist interpreter highlighted a subset of ROI for further review. Imaging and local reporting on these 23 + 9 = 32 patients was sent to a panel of two radiologists and one oncologist, together serving as a reference source against which to quantify positive percent agreement (PPA, representing patients classified as positive by both the panel and an oncologist), negative percent agreement (NPA; representing patients classified as negative by both the panel and an oncologist), and the 95% confidence interval (95%Cl) for overall percent agreement (OPA; prevalence-biased PPA and NPA aggregation). Compared to chance performance (50%), the 95% CI for OPA was 41% to 76%. PPA was 14/18=78% and NPA was 5/14=36%.

Because reevaluation at the patient level was derived from reporting at a lower ROI level, panel agreement was also evaluated at this level. The panel examined only the subset of ROI that had been highlighted by the report evaluators. Of 111 total ROIs highlighted on the TRAQInform Report, the panel agreed with the device classification (new, increasing, unchanged, decreasing, and disappearing) in 37/53=70%, 18/26=70%, 11/13=85%, 5/8 (63%), and 10/11 (90%) ROIs, respectively.

Comparison of Technological Characteristics to Predicate

TRAQinform IQ is a modification of the predicate device. Both the subject and predicate devices are intended to be used by trained medical professionals as an aid in evaluation and information management of digital medical images. Specifically, both devices are radiological image processing systems intended to assess patient radiological scans to provide supplemental information to physicians on changes to regions of interest (ROI).

Comparison of key technological features are provided in Table 3.

Conclusion

The TRAQinform IQ has the same intended use and similar indications, technological characteristics, and principles of operation as the predicate device. Differences do not alter the intended use of the device and do not raise different questions of safety and effectiveness when used as labeled. The performance data demonstrate that the TRAQinform IQ is as safe and effective as the QTxl. Thus, the TRAQinform IQ is substantially equivalent.

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Table 3: Substantial Equivalence Table

Specification /Characteristic	TRAQinform IQSubject Device	Quantitative Total ExtensibleImaging (QTxl) (K173444)Predicate Device
Product Code	LLZ	LLZ
RegulationNumber	892.2050	892.2050
Regulatory Class	II	II
Review Panel	Radiology	Radiology
Users	Trained medical professionals including, butnot limited to, radiologists, oncologists,nuclear medicine physicians, medical imagingtechnologists, dosimetrists and physicists.	Trained medical professionalsincluding, but not limited to,radiologists, oncologists, nuclearmedicine physicians, medical imagingtechnologists, dosimetrists andphysicists.
Intended Use	A software tool to aid in evaluation andinformation management of digital medicalimages.	A software tool to aid in evaluationand information management of digitamedicalimages.
Indications forUse	TRAQinform IQ is a software only device thatprovides quantitative analysis of lesionsidentified as Regions of Interest (ROI) inPET/CT DICOM compliant imaging dataacquired, interpreted, and reported on per localpractice prior to device use.Clinicians responsible for patient care and forordering TRAQinform Reports as an adjunct tolocally reported image interpretation do notinteract directly with the device. Cliniciansresponsible for local image interpretation donot interact with the device and generate theirreporting before and independently of theTRAQinform Report.The TRAQinform Report is generated by thedevice manufacturer and signed by a U.S.board certified physician responsible forsupervising central report generation andqualified to practice nuclearradiology/medicine. The TRAQinform Report isfor use by trained medical professionalsincluding but not limited to oncologists, nuclear	Quantitative Total Extensible Imaging(QTxl) is a software tool used to aid inevaluation and information managementof digital medical images by trainedmedical professionals including, but notlimited to, radiologists, oncologists,nuclear medicine physicians, medicalimaging technologists, dosimetrists andphysicists. The medical modalities ofthese medical images include, but arenot limited to, DICOM CT and PET assupported by ACR/NEMA DICOM 3.0.
Specification /Characteristic	TRAQinform IQSubject Device	Quantitative Total Extensible Imaging(QTxI) (K173444)Predicate Device
	TRAQinform IQ software contains thefollowing functionalities:Automated matching of ROI betweenpreviously performed CT and PET/CTDICOM 3.0 volumetric medicalimages. In order to perform automatedmatching of ROI and quantitativeanalysis of previously performed CTand PET/CT DICOM 3.0 volumetricmedical images, the software initiallyperforms the following functions: Machine learning skeletal andanatomic structuresegmentation. Threshold-based ROIidentification and contouring. Automated quantitative analysis toassess previously performed CT andPET/CT DICOM 3.0 volumetricmedical images, including: change intotal volume and density of eachidentified ROI, and change inFludeoxyglucose F18 (FDG) traceruptake of each identified ROI amongimages. Generation of images of the anatomycombined with spatial and quantitativeinformation, including computedclassification of quantitative FDG ROIchanges.	QTxI assists in the following indications:Receive, store, retrieve, displayand process digital medicalimages Create, display and print reportsfrom those images Provide medical professionalswith the ability to display,register, and fuse medicalimages Identify Regions of Interest (ROIs)and perform ROI contouringallowing quantitative/statisticalanalysis of full or partial body scans Evaluate quantitative change inROIs (total or partial body;individual ROI within individual) with3D interactive rendering of imageswith highlighted ROIs.
Specification /Characteristic	TRAQinform IQSubject Device	Quantitative Total Extensible Imaging(QTxI) (K173444)Predicate Device
	For multi-timepoint quantitative analysis,recommended use is in adult patients 22years and older with partial or whole-bodyPET/CT acquired following administrationof FDG per approved drug prescribinginformation and with the second FDGadministration separated from the first bya period not to exceed 12 months.
	For single-timepoint quantitative analysis,recommended use is in adult patients 22years and older with partial or whole-bodyPET/CT following administration of FDG,a PSMA targeted PET drug, or a SSTR-targeted PET drug per approved drugprescribing information.
	Discrepancies between TRAQinform IQand local PET/CT reporting have beeninvestigated and use of TRAQinform IQhas not been established for binarypatient level progression or non-progression decisions withoutmultidisciplinary review. Discrepanciesbetween TRAQinform IQ and localPET/CT reporting that could impactpatient care should therefore promptconsultation with subject matter experts(for example, in tumor board), with apatient-centered focus on discrepantimaging regions and with blinded orotherwise neutral adjudication regardinginterpretation/classification source.
	TRAQinform IQ is not intended todiagnose any disease, replace thediagnostic procedures for interpretation ofCT or PET/CT images, recommend anyspecific treatment, nor is it intended toreplace the skill and judgement of aqualified medical professional.
Specification /Characteristic	TRAQinform IQSubject Device	Quantitative Total Extensible Imaging(QTxI) (K173444)Predicate Device
Medical Modalities	CT and PET/CT images as supported byACR/NEMA DICOM 3.0	DICOM, CT, MRI, SPECT and PET assupported by ACR/NEMA DICOM 3.0
Medical Modalities	Radiotracers for multiple timepoints: FDG	Radiotracers: NaF PET
Medical Modalities	Radiotracers for single timepoint: FDG,PSMA-targeted PET drug, or a SSTR-targeted PET drug
Operating System	Secure Cloud Environment with WebBrowser Interface	Windows 7 or Windows 10
Identification ofROI	Automatic Threshold based ROIidentification in bone and anatomicalstructures	Threshold based ROI identification in bone
Importation ofROI contours	TRAQinform IQ allows the importation ofphysician established contours of ROI	No importation functionality
Segmentation	Segmentation of bone to identify skeletalstructures and segmentation ofanatomical structures to identify organs.Segmentation is driven by machinelearning algorithms.	Atlas based segmentation of bone to identifyskeletal structures
QuantitativeAnalysis	Scan-to-scan registration and ROImatching allows automaticquantification of ROIs and assessmentof changes/differences in ROI:Changes in ROI shapeSingle ROI splitting into multiple ROIMultiple ROI combining into a single ROIROI appearing, disappearing, and re- appearing across imagesQuantitative analysis of functional andanatomical data for CT and PET/CTscans, including:Volume of each identified ROI on each image	Scan-to-scan registration and ROI matchingallows automatic quantification of ROIs andassessment of changes/differences in ROI.Quantitative/statistical analysis of full orpartial-body scans
	Change in volume of each identified ROI among imagesTotal volume of all identified ROI on each image
Specification /Characteristic	TRAQinform IQSubject Device	Quantitative Total Extensible Imaging(QTxI) (K173444)Predicate Device
	Change in total volume of all identified ROI on each image Heterogeneity of change in volume of each identified ROI For PET scans: Tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of each identified ROI on each image Change in tracer uptake (SUVmax, SUVtotal, SUVmean) of each identified ROI among images Total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image Change in total tracer uptake (SUVmax, SUVtotal, SUVmean, SUVhetero) of all identified ROI on each image Heterogeneity of change in tracer uptake (SUVhetero) among identified ROI For CT scans: Radio density (HUmax, HUtotal, HUmean, HUhetero) for each identified ROI on each image Changes in radio density (HUmax, HUtotal, HUmean) of all identified ROI on each image Change in total radio density (HUmax, HUtotal, HUmean, HUhetero) of all identified ROI on each image Heterogeneity of change in radio density (HUhetero) among identified ROI 2D Graphical renderings of medical images, including Maximum Intensity Projections of the PET and CT, with overlayed and labeled/color-coded ROI for inclusion in TRAQinform Reports.
	3D labeled contours for ROI, anatomic structures, and skeletal structures.

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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).