Commercial Artificial Intelligence (AI) Tool for Screening Digital Breast Tomosynthesis: Factors Associated With AI-Based Breast Cancer Detection.

BACKGROUND. Research on artificial intelligence (AI)-based computer-assisted detection and diagnosis (CADe/CADx) algorithms has focused primarily on digital mammography rather than on digital breast tomosynthesis (DBT). Additionally, DBT-related studies have not comprehensively stratified performance by cancer characteristics. OBJECTIVE. This study's purpose was to evaluate factors associated with cancer detection for a commercial AI-based CADe/CADx algorithm for DBT interpretation. METHODS. This retrospective study included consecutive screening DBT examinations performed from January 2016 to June 2019 that were classified in the institution's breast imaging reporting system as true-positive (n = 500), false-negative (n = 100), true-negative (n = 4400), or false-positive (n = 2500) cases based on radiologists' clinical interpretations and 1-year follow-up outcomes. A commercial AI-based CADe/CADx DBT algorithm (Genius AI Detection 2.0 software, Hologic) analyzed examinations for investigational purposes. A breast imaging radiologist reviewed radiologist true-positive and false-negative examinations with positive AI results to determine whether AI-annotated lesions corresponded to the locations of diagnosed cancers. Factors associated with AI detection were evaluated. RESULTS. The study included 7500 patients (mean age, 59 ± 12 [SD] years) who underwent 7500 DBT examinations. AI detected and correctly localized cancers in 89.8% (449/500) of radiologist true-positives and 32.0% (32/100) of radiologist false-negatives. AI correctly categorized 55.1% (2426/4400) of radiologist true-negatives and 38.9% (972/2500) of radiologist false-positives as negative. Among radiologist true-positives, AI detected and correctly localized 92.4%, 81.6%, 86.7%, and 85.7% of invasive ductal carcinomas, invasive lobular carcinomas, other invasive carcinomas, and cases of ductal carcinoma in situ, respectively (p = .049); AI detected and correctly localized 84.4%, 91.5%, and 95.2% of grade 1, 2, and 3 invasive carcinomas, respectively (p = .03). Among radiologist false-negatives, AI detected and correctly localized 41.2% and 8.3% of cancers with versus without a mammographic finding reported during later diagnostic workup (p = .04). AI detection showed no significant association with age, race, breast density, mammographic finding type, tumor size at surgery, hormone receptor status, or lymph node involvement in either group (p > .05). CONCLUSION. AI detected and correctly localized 89.8% of radiologist true-positive and 32.0% of radiologist false-negative cases. Certain cancer characteristics were associated with AI detection. CLINICAL IMPACT. The results may help radiologists understand the algorithm's strengths and limitations and inform algorithm refinement efforts.