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Paige Prostate is a software only device intended to assist pathologists in the detection of foci that are suspicious for cancer during the review of scanned whole slide images (WSI) from prostate needle biopsies prepared from hematoxylin & eosin (H&E) stained formalinfixed paraffin embedded (FFPE) tissue. After initial diagnostic review of the WSI by the pathologist, if Paige Prostate detects tissue morphology suspicious for cancer, it provides coordinates (X,Y) on a single location on the image with the highest likelihood of having cancer for further review by the pathologist.

Paige Prostate is intended to be used with slide images digitized with Philips Ultra Fast Scanner and visualized with Paige FullFocus WSI viewing software.

Paige Prostate is an adjunctive computer-assisted methodology and its output should not be used as the primary diagnosis. Pathologists should only use Paige Prostate in conjunction with their complete standard of care evaluation of the slide image.

Paige Prostate is an in vitro diagnostic medical device software, derived from a deterministic deep learning system that has been developed with digitized WSIs of H&E stained prostate needle biopsy slides.

Paige Prostate utilizes several accessory devices as shown in Figure 1 below, for automated ingestion of the input. The device identifies areas suspicious for cancer on the input WSIs. For each input WSI, Paige Prostate automatically analyzes the WSI and outputs the following:

• . Binary classification of suspicious or not suspicious for cancer based on a pre-defined threshold on the neural network output.
• . If the slide is classified as suspicious for cancer, a single coordinate (X,Y) of the location with the highest probability of cancer on an image determined to be suspicious for cancer.

Here's a breakdown of the acceptance criteria and the study details for Paige Prostate, based on the provided text:

Acceptance Criteria and Reported Device Performance

Study Information

2. Sample size used for the test set and the data provenance:

• Algorithm Localization and Accuracy Study:

  • Test Set Size: 728 WSIs (311 cancer, 417 benign) from unique patients.
  • Provenance: De-identified WSIs from:
    • Consecutive prostate cancer slides from an internal site (located in US).
    • Challenging cancer slides (≤0.5mm tumor) from an internal site.
    • Consecutive cancer slides submitted from external sites.
    • Challenging cancer slides submitted from external sites.
    • Benign slides from consecutive prostate biopsy cases from an internal site.
    • Consecutive benign slides submitted from external sites (submitted to internal site for expert consultation).
  • External Sites: Included 217 different sites located throughout the world (including US).
  • Retrospective/Prospective: Retrospective.

• Precision Study:

  • Test Set Size: 35 cancer WSIs and 36 benign WSIs from unique patients.
  • Provenance: Slides from an internal site and external sites (217 different sites).
  • Retrospective/Prospective: Retrospective.

• Clinical Study:

  • Test Set Size: 527 WSIs (171 prostate cancer, 356 benign) from unique patients.
  • Provenance: 44.15% from cases prepared, reviewed, diagnosed, and digitized at the internal site (US). 55.85% from cases prepared at 156 different external sites but reviewed, diagnosed, and digitized at the internal site.
  • Retrospective/Prospective: Retrospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Algorithm Localization and Accuracy Study (Localization Ground Truth):

  • Number of Experts: 3 study pathologists.
  • Qualifications: US board-certified pathologists (2 completed anatomic pathology fellowship and 1 sub-specialized genitourinary pathologist). They were blinded to Paige Prostate results.

• Clinical Study (Ground Truth for slide-level cancer/benign):

  • Number of Experts: Not explicitly stated as "experts for ground truth creation" but implies the original pathologists who generated the synoptic diagnostic reports.
  • Qualifications: Pathologists at the internal site generating synoptic diagnostic reports.

4. Adjudication method for the test set:

• Algorithm Localization and Accuracy Study (Localization Ground Truth):

  • Adjudication Method: The union of annotations between at least 2 of the 3 annotating pathologists was used as the localization ground truth.

• Clinical Study (Slide-Level Cancer/Benign Ground Truth):

  • Adjudication Method: "Synoptic diagnostic reports from the internal site were used to generate the ground truth for each slide as either cancer or no cancer." This implies a single, established diagnostic report rather than a consensus process for the study's ground truth.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• Yes, an MRMC comparative effectiveness study was done (the "Clinical Study").
• Effect Size of Improvement:
  • Average Improvement in Sensitivity: 7.3% (95% CI: 3.9%; 11.4%)
  • Average Difference in Specificity: 1.1% (95% CI: -0.7%; 3.4%)
  • The document clarifies that this is an average across 16 pathologists.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Yes, a standalone performance study was done. This is detailed in the "Analytical Performance" section, specifically the "Algorithm Localization (X,Y Coordinate) and Accuracy Study."
  • Sensitivity (Standalone): 94.5%
  • Specificity (Standalone): 94.0%

7. The type of ground truth used:

• Algorithm Localization and Accuracy Study (Slide-Level Cancer Ground Truth): Synoptic pathology diagnostic reports from the internal site.
• Algorithm Localization and Accuracy Study (Localization Ground Truth): Consensus of 3 US board-certified pathologists who manually annotated image patches.
• Precision Study (Slide-Level Cancer Ground Truth): Synoptic diagnostic reports from the internal site.
• Clinical Study (Slide-Level Cancer/Benign Ground Truth): Original diagnostic synoptic reports.

8. The sample size for the training set:

• Training Dataset: 33,543 slide images.

9. How the ground truth for the training set was established:

• "De-identified slides were labeled as benign or cancer based on the synoptic diagnostic pathology report."

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