aceEnhancing diagnostic precision for BI-RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles
Background Breast Imaging Reporting and Data System (BI-RADS) 4a nodules represent a diagnostic dilemma, with a malignancy rate ranging from 2% to 10%. The majority of these nodules prove benign after biopsy, leading to unnecessary invasive procedures, patient anxiety, and healthcare costs. Current clinical practice lacks a reliable, noninvasive tool to accurately distinguish benign from malignant BI-RADS 4a lesions. Emerging evidence suggests that integrating multiparametric ultrasound data with clinical factor…
A LightGBM-based multimodal model integrating ultrasound radiomics, hemodynamic signatures and clinical data achieved high diagnostic accuracy on BI-RADS 4a nodules, supporting safer reduction of unnecessary biopsies.
Evidence
- Peer-reviewedJournal of Applied Clinical Medical Physics2026-10-01
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Truvace Impact Record TRV-2026-1239, v1: “Enhancing diagnostic precision for BI-RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles.” Truvace, 2026-10-01. /record/TRV-2026-1239 (accessed at citation time). sha256 1ef02cc104a7ca8b…
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