Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection

• URL: http://arxiv.org/abs/2503.13581v1
• Date: Mon, 17 Mar 2025 17:17:36 GMT
• Title: Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection
• Authors: Beatrice Brown-Mulry, Rohan Satya Isaac, Sang Hyup Lee, Ambika Seth, KyungJee Min, Theo Dapamede, Frank Li, Aawez Mansuri, MinJae Woo, Christian Allison Fauria-Robinson, Bhavna Paryani, Judy Wawira Gichoya, Hari Trivedi,
• Abstract summary: This study presents a granular evaluation of the Lunit INSIGHT model on a large retrospective cohort of 163,449 screening mammography exams.<n>Performance was found to be robust across demographics, but cases with non-invasive cancers were associated with significantly lower performance.
• Score: 5.089670339445636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detailed subgroup evaluations performed to assess the strengths and weaknesses of commercial models for digital breast tomosynthesis (DBT) imaging. This study presents a granular evaluation of the Lunit INSIGHT DBT model on a large retrospective cohort of 163,449 screening mammography exams from the Emory Breast Imaging Dataset (EMBED). Model performance was evaluated in a binary context with various negative exam types (162,081 exams) compared against screen detected cancers (1,368 exams) as the positive class. The analysis was stratified across demographic, imaging, and pathologic subgroups to identify potential disparities. The model achieved an overall AUC of 0.91 (95% CI: 0.90-0.92) with a precision of 0.08 (95% CI: 0.08-0.08), and a recall of 0.73 (95% CI: 0.71-0.76). Performance was found to be robust across demographics, but cases with non-invasive cancers (AUC: 0.85, 95% CI: 0.83-0.87), calcifications (AUC: 0.80, 95% CI: 0.78-0.82), and dense breast tissue (AUC: 0.90, 95% CI: 0.88-0.91) were associated with significantly lower performance compared to other groups. These results highlight the need for detailed evaluation of model characteristics and vigilance in considering adoption of new tools for clinical deployment.

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