Volumetric phase-specific three-dimensional radiomics and machine learning for differentiation of non-mass enhancement in breast magnetic resonance imaging

PURPOSE: ) post-contrast phases to differentiate benign from malignant NMEs. METHODS: A total of 110 NMEs (86 benign and 24 malignant) from 108 patients were analyzed. Radiological features were recorded. Radiomics features were extracted from manual 3D segmentations using LIFEx software. Multivariate logistic regression (LR) and supervised ML algorithms-LR, support vector machine, random forest, and gradient boosting-were applied. The methodological quality was assessed using the Multicenter Evaluation of Radiomics in Clinical Studies framework. RESULTS: < 0.05). In the early phase, combining all radiomics features increased specificity from 78% to 98% and accuracy from 82% to 93%. ML models further improved performance, achieving specificity up to 99% and area under the curve (AUC) values exceeding 0.91. Similar improvements were observed on the late phase, with accuracies up to 91% and AUC values up to 0.93. CONCLUSION: Volumetric 3D radiomics, combined with ML, using early and late post-contrast phases improves diagnostic accuracy and specificity for NME on breast MRI. CLINICAL SIGNIFICANCE: Integrating 3D radiomics and ML into breast MRI evaluation supports more accurate decision-making in NME and may reduce unnecessary biopsies.