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BU-CAD is a software application indicated to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions suspicious for breast cancer who are being referred for further diagnostic ultrasound examination.

Output of the device includes regions of interest (ROIs) and lesion contours placed on breast ultrasound images assisting physicians to identify suspicious soft tissue lesions from up to two orthogonal views of a single lesion, and region-based analysis of lesion malignancy upon the physician's query. The region-based analysis indicates the score of lesion characteristics (SLC), and corresponding BI-RADS categories in user-selected ROIs or ROIs automatically identified by the software. In addition, BU-CAD also automatically classifies lesion shape, orientation, margin, echo pattern, and posterior features according to BI-RADS descriptors.

BU-CAD may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report (SR).

Patient management decisions should not be made solely on the basis of analysis by BU-CAD.

Limitations: BU-CAD is not to be used on sites of post-surgical excision, or images with Doppler, elastography, or other overlays present in them. BU-CAD is not intended for the primary interpretation of digital mammography images. BU-CAD is not intended for use on mobile devices.

BU-CAD developed by TaiHao Medical Inc. is a software system designed to assist users in analyzing breast ultrasound images including identification of regions suspicious for breast cancer and assessment of their malignancy. The system consists of a Viewer, a Lesion Identification Module, and a Lesion Analysis Module. The Viewer loads breast ultrasound and mammography images from local storage or PACS for review, and includes tools for measurement and image adjustment. The Lesion Identification Module identifies automated ROIs and generates lesion contours on breast ultrasound images. The Lesion Analysis Module analyzes given ROIs and generates a score of lesion characteristics (SLC), BI-RADS category, and BI-RADS descriptors. Users can replace automated ROIs with re-delineated rectangular ROIs for analysis. The last analysis results are displayed and modifiable by the user. BU-CAD also supports exporting CAD results to third-party reporting software.

The provided text describes the acceptance criteria and the study proving the device meets these criteria for the BU-CAD system.

Here's the breakdown of the information requested:

1. Table of Acceptance Criteria and Reported Device Performance

The document implies acceptance criteria by demonstrating performance gains in a comparative study against human readers. While explicit quantitative acceptance criteria for each metric are not stated, the success is determined by statistically significant improvement over unaided human performance and comparable performance to predicate devices. The primary acceptance criterion appears to be superiority of aided performance (AUC_LROC) over unaided performance.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:
  • MRMC Reader Study: 628 cases
  • Standalone Study: 1139 cases (which includes the 628 reader study cases plus 511 additional extended cases).
• Data Provenance:
  • MRMC Reader Study: 456 cases from the United States, 172 cases from Taiwan.
  • Standalone Study: 531 cases from North America, 36 cases from Europe, 572 cases from Taiwan.
• Retrospective or Prospective: The study is clearly stated as a retrospective study ("fully crossed multi-reader multi-case receiver operating characteristic (MRMC-ROC) retrospective study").

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

The document does not explicitly state the number of experts used to establish the ground truth for the test set. However, it mentions an "expert panel" in the context of defining ground truth ROIs for the robustness experiments.

Qualifications of Experts (Readers in MRMC study, likely similar for GT):

• 16 Readers participated in the MRMC study.
• Specialties: 14 Radiologists, 2 Breast Surgeons.
• Experience: Ranged from 1 year to >30 years of experience (as a radiologist/breast surgeon).
• Certifications/Training: Most radiologists (13/14) were MQSA certified. 4/14 radiologists had received Breast Image Fellowship training.

4. Adjudication Method for the Test Set

The document does not explicitly detail the adjudication method for establishing the definitive ground truth for the test set cases (e.g., how malignancy/benignity or BI-RADS categories were finalized if there were disagreements among initial assessments). However, it mentions "Pathology proof benign," "Two-year follow-up benign," and specific malignant pathology types (DCIS, IDC, ILC, Other cancer types) as the basis for benign/malignant case classification in the dataset demographics. This suggests pathology and clinical follow-up as the primary ground truth, not necessarily a reader adjudication process per se, for the malignancy outcome.

For the reader study itself, it was a "fully crossed" MRMC-ROC study where readers evaluate cases independently, both unaided and aided. The performance metrics (AUC, sensitivity, specificity, etc.) are derived from comparing individual reader's interpretations against the established 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.
• Effect Size of Improvement:
  • Primary Objective (AUC_LROC Shift): The mean AUC_LROC shift was +0.0374. (Unaided AUC: 0.7786, Aided AUC: 0.8160). This improvement was statistically significant (p-value = 0.0001).
  • Comparison to Predicates: This shift (+0.0374) was stated to be "similar" to Koios DS for Breast (+0.037) and TransparaTM (+0.02).
  • Other Metrics (Aided vs Unaided):
    • Sensitivity: Increased from 0.9225 to 0.9353.
    • Specificity: Increased from 0.3165 to 0.3611.
    • NPV (unadjusted): Increased from 0.8623 to 0.8945.
    • PPV (unadjusted): Increased from 0.4876 to 0.5056.
    • Reduction in False Positives: A net benefit of +267 events (FP to TN) indicating reduction of false positives across all readers for benign cases.
    • Interpretation Time: Decreased by approximately 40%.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone study was done.
• Standalone Performance (AUC_LROC):
  • On the 628 reader study cases: 0.7987 (95% CI: 0.7626, 0.8348)
  • On the larger 1,139 standalone study cases: 0.8203 (95% CI: 0.7947, 0.8458)
• Standalone Sensitivity & Specificity (using BI-RADS 4A as cutoff):
  • Sensitivity: 88.33% (439/497)
  • Specificity: 57.94% (372/642)
• Lesion Identification Module (CADe) Accuracy: 93.24% (1062/1139) detected and localized correctly.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The ground truth for the test set (both reader study and standalone study cases) was primarily established by:

• Pathology Proof: For malignant cases (Ductal carcinomas in situ (DCIS), invasive ductal carcinoma (IDC), Invasive lobular carcinoma (ILC), and other cancer types) and some benign cases.
• Two-year Follow-up: For other benign cases.

This indicates a strong, objective ground truth based on definitive clinical outcomes.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It only states that the "testing dataset was not used for training of BU-CAD algorithms."

9. How the Ground Truth for the Training Set Was Established

The document does not provide information on how the ground truth for the training set was established. Since the test set ground truth was largely based on pathology and follow-up, it is highly probable that the training data followed similar rigorous ground truth establishment methods, but this is not explicitly stated.

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