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PowerLook® Tomo Detection V2 Software is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT systems. The system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting physician.

PLTD V2 detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) image. The PLTD V2 software allows a interpreting physician to quickly identify suspicious soft tissue densities and calcifications by marking the detected areas in the tomosynthesis images. When the PLTD V2 marks are displayed by a user, the marks will appear as overlays on the tomosynthesis images. The PLTD V2 marks also serve as a navigation tool for users, because each mark is linked to the tomosynthesis plane where the detection was identified. Users can navigate to the plane associated with each mark by clicking on the detection mark. Each detected region will also be assigned a "score" that corresponds to the PLTD V2 algorithm's confidence that the detected region is a cancer (Certainty of Finding Score). Certainty of Finding scores are relative scores assigned to each detected region and a Case Score is assigned to each case regardless of the number of detected regions. Certainty of Finding and Case Scores are computed by the PLTD V2 algorithm and represent the algorithm's confidence that a specific finding or case is malignant. The scores are represented on a 0% to 100% scale. Higher scores represent a higher algorithm confidence that a finding or case is malignant. Lower scores represent a lower algorithm confidence that a finding or case is malignant.

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

1. Acceptance Criteria and Reported Device Performance

The device is a Computer-Assisted Detection and Diagnosis (CAD) software for digital breast tomosynthesis (DBT) exams. The acceptance criteria are largely demonstrated through the multi-reader multi-case (MRMC) pivotal reader study and standalone performance evaluations.

Table of Acceptance Criteria and Reported Device Performance:

2. Sample Sizes and Data Provenance

• Pivotal Reader Study (Test Set):

  • Sample Size: 260 Hologic digital breast tomosynthesis (DBT) cases. This included 65 cancer cases with 66 malignant lesions.
  • Data Provenance: Retrospective. The country of origin is not explicitly stated, but given the FDA submission, it's likely primarily from the US or regions with similar regulatory standards.

• Standalone Performance Studies (Test Set):

  • Hologic DBT: 655 Hologic DBT cases, including 235 cancer cases with 242 malignant lesions.
  • GE DBT: 610 GE DBT cases, including 204 cancer cases with 221 malignant lesions.
  • Data Provenance: Retrospective. No country of origin specified.

• Training Set Sample Size:

  • The document does not explicitly state the sample size for the training set. It mentions the algorithm uses "deep learning technology," implying a substantial training set would have been used.

3. Number of Experts and Qualifications for Test Set Ground Truth

• Pivotal Reader Study & Standalone Studies: The document does not explicitly state the number of experts used to establish the ground truth for the test sets. However, for the pivotal reader study, it states: "The purpose of the pivotal study was to compare clinical performance of radiologists using CAD... to that of radiologists using DBT without CAD." This implies the radiologists in the study were the interpreting physicians who would typically establish ground truth in a clinical setting based on their diagnostic judgment and follow-up (biopsy, etc.). It mentions that the ground truth for cancer cases required "malignant lesion localization."

• Qualifications of Experts: The readers in the pivotal study were "24 tomosynthesis radiologist readers." While specific experience levels (e.g., "10 years of experience") are not provided, "radiologist" implies medical doctors specialized in radiology, typically with several years of post-medical school training and board certification.

4. Adjudication Method for Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1 consensus) for establishing the ground truth for the test sets. It implies that the ground truth for malignancy was established via biopsy or other confirmed pathology (referred to as "malignant lesions" and "cancer cases"). For the reader study, the "ground truth" for evaluating reader performance was the confirmed cancer status of the cases and lesions.

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

• Yes, an MRMC comparative effectiveness study was done. This was the "Pivotal Reader Study."
• Effect Size of Human Reader Improvement with AI vs. Without AI Assistance:
  • AUC: Radiologists' per-subject average AUC improved by 0.057 when using CAD (0.852 with CAD vs. 0.795 without CAD).
  • Reading Time: Reading time improved 52.7% with CAD.
  • Case-Level Sensitivity: Average sensitivity increased by 0.080 (0.850 with CAD vs. 0.770 without CAD).
  • Lesion-Level Sensitivity: Average sensitivity increased by 0.084 (0.853 with CAD vs. 0.769 without CAD).
  • Case-Level Specificity: Average specificity increased by 0.069 (0.696 with CAD vs. 0.627 without CAD).
  • Recall Rate in Non-Cancer Cases: Average recall rate was reduced by 0.072 (0.309 with CAD vs. 0.380 without CAD).

6. Standalone Performance (Algorithm Only without Human-in-the-Loop Performance)

• Yes, standalone studies were done.
  • A standalone study was conducted for Hologic DBT cases (N=655).
  • A standalone study was conducted for GE DBT cases (N=610).
  • A comparison of performance between GE DBT and Hologic DBT was also performed to show non-inferiority.

7. Type of Ground Truth Used

The ground truth used for the cancer cases (in both standalone and reader studies) appears to be pathology-confirmed malignancy (referred to as "cancer cases" and "malignant lesions"). The document states that "malignant lesion localization was required for a reader to correctly detect cancer in a case," implying confirmed cancerous findings.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions the use of "deep learning technology," which typically requires large datasets for training.

9. How Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. Given it's a deep learning-based CAD system for cancer detection, it's highly probable that ground truth for the training data would also have been established through pathology-confirmed diagnoses (biopsy results) and/or by expert radiologists' review and consensus with prolonged follow-up to confirm benign status.

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