QP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.

Device Description

QP-Prostate® CAD is an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software. QP-Prostate® CAD uses Al-based algorithms trained with pathology data to detect suspicious lesions for clinically significant prostate cancer. The device automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI and provides marks over regions of the images that may contain suspected lesions. There are two possible markers that are provided in different colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high suspicion level).

The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Based on biparametric input consisting of T2W and DWI series, the analysis is run automatically, and the output in standard DICOM formats is returned to PACS.

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 Reported Device Performance

Table 1: Acceptance Criteria and Reported Device Performance (Standalone)

Metric (lesion level)	Acceptance Criterion (Implicit)	Reported Device Performance
AUC-ROC	Evidence of good discriminatory ability (e.g., above a certain threshold)	0.732 (95% CI: 0.668-0.791)
Sensitivity (high suspicion marker)	Evidence of good detection rate for clinically significant findings	0.677 (95% CI: 0.593-0.761)
False Positive Rate per Case (high suspicion marker, any biopsy source)	Evidence of acceptable false positive rate	0.417 (95% CI: 0.313-0.522)
Sensitivity (high and moderate suspicion markers)	Evidence of good detection rate for clinically significant findings	0.795 (95% CI: 0.722-0.861)
False Positive Rate per Case (high and moderate suspicion markers, any biopsy source)	Evidence of acceptable false positive rate	0.855 (95% CI: 0.709-0.996)

Note: The document does not explicitly state numerical acceptance criteria thresholds for the standalone performance metrics (AUC-ROC, Sensitivity, FPR). Instead, it presents the results and implies that these values "demonstrate the safety and effectiveness" in comparison to the predicate device. The general implicit acceptance criterion for these metrics would be that they exhibit performance levels indicative of a useful diagnostic aid.

Table 2: Acceptance Criteria and Reported Device Performance (Multi-Reader Multi-Case Study)

Metric	Acceptance Criterion (Explicit)	Reported Device Performance
ΔAUC (AUCaided - AUCunaided) (Primary Endpoint)	A statistically significant improvement (p-value < 0.05)	0.019 (95% CI: 0.001-0.038) p-value: 0.039
Sensitivity with/without CAD assistance (Secondary Endpoint)	Improvement when using CAD assistance	Not explicitly quantified in table, but overall improvement is stated to be demonstrated.
Specificity with/without CAD assistance (Secondary Endpoint)	Improvement when using CAD assistance	Not explicitly quantified in table, but overall improvement is stated to be demonstrated.

The document stated directly that "The test results demonstrate that QP-Prostate® CAD functioned as intended and met its primary endpoint, is acceptable for clinical use, and is as safe and effective as its predicate device, without introducing new questions of safety and efficacy."

Study Details for QP-Prostate® CAD performance:

Sample Size and Data Provenance for Test Set:
- Sample Size: 228 cases for the clinical reader performance assessment (MRMC study) and 247 for the standalone performance assessment (lesion-level).
- Data Provenance: Retrospectively collected from multiple centers across the US. This dataset was "completely independent from the training dataset that was acquired from different institutions."
Number of Experts and Qualifications for Ground Truth (Test Set):
- The document states that the ground truth for the standalone performance evaluation was derived from "associated pathology reports and radiologist interpretations." It does not specify the number or qualifications of radiologists involved in these interpretations for the ground truth establishment of the test set.
- For the MRMC study, 9 readers (presumably radiologists, though specific qualifications for each weren't detailed beyond "clinical readers") participated in the study itself, but this is about their performance, not their role in establishing the ground truth.
Adjudication Method for Test Set:
- The document doesn't explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth of the test set cases based on radiologist interpretations. It mentions "biopsy outcomes" and "biopsy confirmed (Gleason score ≥ 7)" for positive cases, and "biopsy-confirmed (Gleason score < 7) or non-biopsied with a clinical followup of at least one year" for negative cases. This suggests pathology and long-term clinical follow-up played a primary role in ground truth for patient status.
Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- Was it done? Yes, a fully crossed multi-reader multi-case study was performed.
- Effect Size of Human Reader Improvement: The primary endpoint was an improvement in AUC.
  - AUC_unaided: 0.849 (95% CI: 0.814-0.884)
  - AUC_aided: 0.868 (95% CI: 0.834-0.902)
  - ΔAUC (AUC_aided - AUC_unaided): 0.019 (95% CI: 0.001-0.038), p-value: 0.039.
  - This indicates a small but statistically significant improvement in reader performance (AUC) when assisted by the AI.
Standalone Performance Study (Algorithm Only):
- Was it done? Yes, a standalone performance assessment was conducted.
- The results are summarized in "Table 2: Summary of the standalone performance testing for QP-Prostate® CAD" (refer to the table above).
Type of Ground Truth Used:
- For the Test Set:
  - Pathology: Biopsy confirmed for both positive (Gleason score ≥ 7) and negative (Gleason score < 7) cases of clinically significant prostate cancer (csPCa).
  - Outcomes Data: For non-biopsied negative cases, ground truth was established by "a clinical followup of at least one year."
  - Expert Consensus/Radiologist Interpretation: Used in conjunction with pathology for the standalone lesion-level evaluation ("QP-Prostate® CAD outputs were compared to ground truth diagnoses derived from associated pathology reports and radiologist interpretations, at lesion-level").
Sample Size for Training Set:
- The exact sample size for the training set is not explicitly stated in numerical form. It is described as "cases acquired in the US from multiple centers."
How Ground Truth for Training Set Was Established:
- The AI-based algorithms were "trained with biopsy outcomes as ground truth to detect suspicious lesions for csPCa (Gleason score ≥7)." This indicates that pathology reports (biopsy outcomes) were the primary source of ground truth for the training data.

Summary

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Image /page/0/Picture/0 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 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.

March 18, 2025

Quibim S.L. % John J. Smith Partner Hogan Lovells US LLP 555 13th St NW Washington, District of Columbia 20004

Re: K242683

Trade/Device Name: QP-Prostate® CAD Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: ODO Dated: February 14, 2025 Received: February 14, 2025

Dear John J. Smith:

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

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

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"

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(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 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.

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 Rue"). 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.

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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 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.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

Enclosure

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Indications for Use

510(k) Number (if known) K242683

Device Name QP-Prostate® CAD

Indications for Use (Describe)

OP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.

Intended patient population: Patients above 40 years with prostate MR imaging.

Intended users:

QP-Prostate® CAD is intended to be used by physicians with proper training, including radiologists, urologists and any physician qualified to read and interpret prostate MRI consistent with ACR recommendations in the context of PI-RADS.

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

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

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510(k) SUMMARY Quibim's QP-Prostate® CAD (K242683)

Submitter Quibim S.L. Avenida Aragon 30, 13th floor, Office I-J, 46021 Valencia (Spain)

Phone:

. +34 961 243 255
Contact Person:
Ángel Alberich Bayarri, CEO and Founder of Quibim .
Josep Hortigüela Zamora, VP of Quality Assurance and Regulatory Affairs .

Date Prepared: March 12, 2025

Name of Device: QP-Prostate® CAD

Common or Usual Name: CAD software for lesions suspicious for clinically significant prostate cancer

Classification Name: 892.2090 radiological computer assisted detection/diagnosis software for lesions suspicious for cancer Requlatory Class: Class II

Product Code: QDQ

Predicate Device

. Applicant: ScanMed, LLC
Device's trade name: ProstatIDTM ●
. 510(k) number: K212783
Product code: QDQ .

Device Description

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Intended Use / Indications for Use

Intended patient population:

Patients above 40 years with prostate MR imaging.

Intended users:

Comparison to predicate device

QP-Prostate® CAD and the predicate device ProstatID™ have very similar indications statements. Both are medical image processing applications intended for concurrent reading for physicians interpreting prostate MRI identifying suspected lesions and assessing likelihood for prostate cancer. Both devices generate a 3D rendition. ProstatID™ provides the regional overlay scores on a continuous scale ranging from 0-1, presented as a colorized translucent overlay, and a suggested level of suspicion or overall PI-RADS score, whereas QP-Prostate® CAD provides overlay markings around the detected suspicious lesions, in two possible colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high).

Summary of Technological Characteristics

At a high level, the subject and predicate devices are based on the following same technological elements:

Both devices are compatible with DICOM standard.
Both devices use the same input sequences to run the analysis: T2-weighted and DWI acquired with specified protocols.

The following technological differences exist between the subject and predicate devices:

· Both devices are artificial intelligence-based, but they differ in algorithm methodology (e.g. ProstatID™ is based on random forest and QP-Prostate® CAD is based on Neural Networks and Machine Learning). This difference could affect its safety or effectiveness but does not raise any different questions of safety or effectiveness, because a set of verification and

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validation tests (performance testing) demonstrate the safety and effectiveness of QP-Prostate® CAD.

A table comparing the key features of the subject and predicate devices is provided below:

Feature	Proposed device:QP-Prostate® CAD	Predicate device:ProstatID™ (K212783)	Comments
REGULATORY DATA
Class	II	II	N/A
Regulation name	Radiological ComputerAssistedDetection/DiagnosisSoftware	Radiological ComputerAssistedDetection/DiagnosisSoftware	N/A
Regulation number	21 CFR 892.2090	21 CFR 892.2090	N/A
Classification Panel	Radiology	Radiology	N/A
Product Code	QDQ	QDQ	N/A
Applicant	Quibim S.L.	ScanMed, LLC.	N/A
		Indications for Use
Medical devicedescription	QP-Prostate® CAD is aComputed AidedDetection andDiagnosis(CADe/CADx) imageprocessing softwarethat automaticallydetects and identifiessuspected lesions in theprostate gland basedon bi-parametricprostate MRI. Thesoftware is intended tobe used as aconcurrent read byclinicians with propertraining in a clinicalsetting as an aid forinterpreting prostateMRI studies. Theresults can bedisplayed in a variety ofDICOM outputs,including identifiedsuspected regions ofabnormalities markedas an overlay ontosource MR images. Theoutput can be displayedon third-party DICOMworkstations andPicture Archive andCommunicationSystems (PACS).Patient management	ProstatIDTM is aradiological computerassisted detection(CADe) and diagnostic(CADx) software devicefor use in a healthcarefacility or hospital toassist trainedradiologists in thedetection, assessment,and characterization ofprostate abnormalities,including cancer lesionsusing MR image datawith the followingindications for use.ProstatID analyzes T2W,DWI and ADC MRI data.ProstatID does notinclude DCE images inits analysis.ProstatID software isintended for use as aconcurrent reading aidfor physiciansinterpretingprostate MRI exams ofpatients presented forhigh-risk screening ordiagnostic imaging, fromcompatible MRIsystems, to identifyregions suspicious forprostate cancer andassess their likelihood ofmalignancy.Outputs of the deviceinclude the volume of theprostate and locations,as well as the extent ofsuspect lesions, with	Substantiallyequivalent. Minordifferences in outputs,exist, but both devicesare CADe/x devicesthat characterizeprostate abnormalitieson MR image data.The differences do notraise differentquestions of safetyand effectiveness.
Feature	Proposed device:	Predicate device:	Comments
	QP-Prostate® CAD	ProstatID™ (K212783)
	decisions should not bebased solely on theresults of QP-Prostate®CAD.	index scores indicatingthe likelihood that canceris present, as well as anexam score by way ofPI-RADS interpretationsuggestion. "Extent ofsuspect lesions" refers toboth the assessment ofthe boundary of aparticular abnormality,as well as identificationof multiple abnormalities.In cases where multipleabnormalities arepresent, ProstatID canbe used to assess eachabnormalityindependently.Outputs of this deviceshould be interpretedwith all available MRdata consistent withACR clinicalrecommendations(e.g., dynamic contractenhanced images ifavailable) in context ofPI-RADS v2, and inconjunction with bi-parametricMRI acquired with eithersurface or endorectalMRI accessory coilsfrom compatible MRIsystems. Analysis byProstatIDis not intended as areplacement forinterpreting prostateabnormalities using MRimage data consistentwith clinicalrecommendations(including DCE); norshould patientmanagement decisionsbe made solely on thebasis of ProstatID.
Intended userpopulation	QP-Prostate® CAD isintended to be used byphysicians with propertraining, includingradiologists, urologistsand any physicianqualified to read andinterpret prostate MRIconsistent with ACRrecommendations in thecontext of PI-RADS.	Intended users ofProstatID are physiciansqualified to read andinterpret prostate MRIexams consistent withACR recommendationsin the context of PI-RADS v2.	Substantiallyequivalent.
Feature	Proposed device:QP-Prostate® CAD	Predicate device:ProstatID™ (K212783)	Comments
Intended patientpopulation	Patients above 40years with prostate MRimaging.	The device is intended tobe used in thepopulation of biologicaladult males with aprostate glandundergoing screening orclinical MRI exams. Thisincludes biological maleswith clinical indicatorssuggestive of possibleprostate cancer orwith family history ofprostate cancer.	Substantiallyequivalent.
CHARACTERISTICS
Clinical finding	Identification of locationof suspicious lesions,with two suspicionlevels of clinicallysignificant prostatecancer, moderate orhigh	Extent and location ofidentified suspectedlesions, assigned to alevel of suspicion (0-1).A suggested level ofsuspicion or overall PI-RADS exam score.	Substantiallyequivalent. Whileminor differences existin that QP-Prostateprovides two suspicionlevels, while ProstatIDassigns a level ofsuspicion on a scale of0-1, the intended useof the devices remainthe same.Furthermore, thesafety andeffectiveness of QP-Prostate are supportedthrough performanceassessments and donot raise differentquestions of safetyand effectiveness.
DICOM compatibilityInput for analysis	DICOM-in DICOM-outT2W and DWI series,DCE is not included foranalysis	DICOM-in DICOM-outT2W, DWI and ADC MRIdata, DCE is notincluded for analysis	SameSubstantially similar.While QP-Prostatetakes a subset of theinput data used in thepredicate device. Thisdifference does notraise differentquestions of safetyand effectiveness.
Algorithm methodology	Neural Networks andMachine Learning	Random Forest	AI-based, safety andeffectiveness aresupportedthrough performanceassessments.
Safety	The software isintended to be used asa concurrent read byclinicians with propertraining in a clinicalsetting as an aid forinterpreting prostateMRI studies. Patientmanagement decisionsshould not be basedsolely on the results ofQP-Prostate® CAD.	ProstatID software isintended for use as aconcurrent reading aid.Analysis by ProstatID isnot intended as areplacement forinterpreting prostateabnormalities using MRimage data consistentwith clinicalrecommendations(including DCE); nor	Same
Feature	Proposed device:QP-Prostate® CAD	Predicate device:ProstatID™ (K212783)	Comments
		should patientmanagement decisionsbe made solely on thebasis of ProstatID.

Table 1: Comparison of key features of QP-Prostate® CAD (Quibim) and the predicate device ProstatID™ (Bot Image, Inc.)

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Model development

QP-Prostate® CAD uses Al-based algorithms trained with biopsy outcomes as ground truth to detect suspicious lesions for csPCa (Gleason score ≥7). The training dataset included cases acquired in the US from multiple centers, with multiple magnetic fields (1.5T, 3T) and vendors (Siemens, GE Medical Systems, Philips Medical Systems). T2w and DWI sequences of these cases were used for training. The dataset contained representation of different Gleason score subgroups, was ethnically diverse, and the age ranged from 43 to 86 years. The fine-tuning of the model aimed to optimize the sensitivity and specificity at a case level for the high and moderate markers. To ensure generalizability and performance of QP-Prostate® CAD, standalone and MRMC performance testing studies have been performed with a dataset sourced from different institutions or sources in the US not part of the model training or development datasets. The description of this performance testing dataset and the results of the standalone and MRMC performance testing studies are presented below.

Performance Data

QP-Prostate® CAD software was developed according to FDA recognized consensus standards for software development. Software verification and validation was performed following V&V plans and protocols verifying that product specifications were met.

Dataset selection

The pivotal dataset included 228 cases collected retrospectively from multiple centers across the US. This dataset was completely independent from the training dataset that was acquired from different institutions. The pivotal dataset included both positive and negative cases in the same proportion. Positive cases were biopsy confirmed (Gleason score ≥ 7); negative cases were either biopsy-confirmed (Gleason score < 7) or non-biopsied with a clinical followup of at least one year.

Cases were acquired with multiple magnetic fields (1.5 T and 3T) and multiple vendors, and met the device's recommended acquisition parameters for the T2w and DWI sequences. The ethnicity distribution in the dataset is representative of the broader US population.

Case demographics:

Age range: 43 to 81 Age mean: 64.7

Asian: 3/228 (1.3%) African American: 19/228 (8.3%) White: 194/228 (85.1 %) Other: 4/228 (1.8%) Declined/unavailable: 8/228 (3.5%)

Hispanic: 7/228 (3.1%)

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Not Hispanic: 206/228 (90.4%) Declined/unavailable: 6/228 (6.6%)

Magnetic field strength:

1.5T: 32/228 (14.0%) 3T: 196/228 (86.0%)

Scanner vendor:

Philips Medical Systems: 46/228 (20.2 %) GE Medical Systems: 40/228 (17.5%) Siemens: 142/228 (62.3%)

Standalone performance assessment

For the performance evaluation. QP-Prostate® CAD outputs were compared to ground truth diagnoses derived from associated pathology reports and radiologist interpretations, at lesion-level, based on targeted biopsy locations (both positives and negatives) and negative cases without biopsy in the database (N=247).

Diagnostic accuracy of QP-Prostate® CAD was assessed with the AUC-ROC at lesion level, as well as with the sensitivity and specificity at lesion level at the operating points determined by high suspicion and moderate suspicion markers.

The results are summarized in the following table:

Metric (lesion level)	Result
AUC-ROC	0.732(95% CI: 0.668-0.791)
Sensitivity(high suspicion marker)	0.677(95% CI: 0.593-0.761)
False Positive Rate per Case(high suspicion marker, any biopsy source)	0.417(95% CI: 0.313-0.522)
Sensitivity(high and moderate suspicion markers)	0.795(95% CI: 0.722-0.861)
False Positive Rate per Case(high and moderate suspicion markers, any biopsy source)	0.855(95% CI: 0.709-0.996)

Table 2: Summary of the standalone performance testing for QP-Prostate® CAD.

Additional subgroup analyses of patient age (≤65, >65), BMI (<25, ≥25), race/ethnicity, magnetic field (1.5T, 3T) and machine vendor (Siemens, Philips, GE) were performed. QP-Prostate® CAD's performance was consistent across all age. BMI, race/ethnicity and machine vendor subgroups. 1.5T subgroup showed a minor deviation in performance, attributed to the inherently lower image quality of 1.5T acquisitions over 3T; 3T acquisitions are preferable when available.

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Clinical reader performance assessment

For the clinical reader performance assessment, a fully crossed multi-reader multi-case study was performed, where each case (N=228) was interpreted by each of the 9 readers with and without QP-Prostate® CAD output in a random-reader random-case factorial design. The clinical reader performance assessment compares the clinical reader performance in identifying clinically significant prostate cancer (Gleason score ≥ 7) when using or not using the device output.

The primary endpoint of the clinical reader performance assessment was the performance advantage of using QP-Prostate® CAD output compared to not using its output for diagnosis of csPCa lesions by clinical readers, determined by the AUCaided versus AUCunaided at case level. The acceptance criterion was a statistically significant improvement.

The secondary endpoint was the performance advantage of using QP-Prostate® CAD output compared to not using its output for diagnosis of csPCa lesions by clinical readers, determined by sensitivity and specificity of the readers with and without QP-Prostate® CAD at case level.

Metric	Result
AUCunaided	0.849(95% CI: 0.814-0.884)
AUCaided	0.868(95% CI: 0.834-0.902)
ΔAUC (AUCaided- AUCunaided)	0.019(95% CI: 0.001-0.038)p-value: 0.039

Table 3: Summary of the clinical reader performance testing results for QP-Prostate® CAD.

The test results demonstrate that QP-Prostate® CAD functioned as intended and met its primary endpoint, is acceptable for clinical use, and is as safe and effective as its predicate device, without introducing new questions of safety and efficacy.

Conclusions

QP-Prostate® CAD is as safe and effective as ProstatID™. QP-Prostate® CAD has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended diagnostic use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between the QP-Prostate® CAD and its predicate devices raise no new issues of safety or effectiveness. Performance data demonstrate that QP-Prostate® CAD is as safe and effective as ProstatID™. Thus, QP-Prostate® CAD is substantially equivalent.

Regulation Number and Section

§ 892.2090 Radiological computer-assisted detection and diagnosis software.

(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.