A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction

Rationale and objectives: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B. Contrast-enhanced mammography (CEM) may improve biopsy outcome prediction for this subpopulation, but machine learning-based analysis of CEM is largely unexplored. We aim to develop a machine learning-based analysis of CEM using computer-extracted radiomics and radiologist-assessed descriptors to predict breast biopsy outcomes of BI-RADS 4A/4B/4C or 5 lesions.

Materials and methods: This HIPPA-compliant, IRB-approved study included women in a single institution who had BI-RADS 4A/4B/4C or 5 lesions and underwent CEM imaging prior to biopsy. Logistic regression models were built to predict biopsy outcomes using radiomics features and four radiologist-assessed qualitative descriptors. A cohort of 201 patients was used for model development/training, and an independent group of 86 patients were used as an internal test set. AUC was used to measure model's performance. Positive predictive value (PPV) was assessed on subgroups of BI-RADS 4A or 4B lesions.

Results: Model AUC was 0.90 for radiomics, 0.81 for clinical descriptors and 0.88 for their combination. On patients with an initial BI-RADS 4A or 4B scores, model combining radiomics and clinical descriptors of pre-biopsy CEM increased PPV3 to 18% from the radiologist's 6% for 4A patients, and to 25% from the radiologist's 17% for 4B patients.

Conclusion: Machine learning models combining radiomics features and clinical descriptors on CEM can predict breast biopsy outcomes on women with BI-RADS 4A/4B/4C or 5 lesions.