Distinguishing Benign from Malignant Small Enhancing Breast Lesions Using Radiomics

Résumé fourni par la source

Background/Objectives: Small enhancing lesions (often termed foci if lesion ≤ 5 mm) observed on breast magnetic resonance imaging (MRI) pose a persistent challenge. Their clinical relevance and the most effective management strategy remain unclear, often leading to unnecessary biopsies. We sought to uncover whether quantitative radiomic features of these lesions could reliably discriminate malignant ones from benign ones. Methods: In this single-center retrospective study (2015–2024), we analyzed 31 contrast-enhancing breast lesions with a maximum diameter ≤ 10 mm measured on a single axial MRI slice from 30 patients who underwent 1.5-T or 3.0-T breast MRI followed by histologic confirmation (10 malignant, 21 benign). Lesions were manually delineated, and 105 radiomic variables were extracted from early-phase dynamic contrast-enhanced (DCE) subtraction images. Feature importance was quantified with a Random Forest model; iterative top-k pruning found the highest-performing variables. A multilayer perceptron (MLP) classifier was trained and validated using a leave-one-subject-out cross-validation (LOSO-CV) scheme, with the sensitivity, specificity, accuracy, and AUC as the primary performance metric. Results: Of the 105 extracted features, 44 carried predictive information (feature importance score ≥ 0.20). Progressive feature reduction yielded an optimal subset of nine radiomic features (six texture-based and three shape-based). Texture-derived features predominated among the most informative variables. The MLP achieved a sensitivity of 0.80, specificity of 0.89, accuracy of 0.86, and AUC of 0.82. Conclusions: In this exploratory study, a preliminary nine-feature radiomic signature extracted from early-phase DCE-subtraction images has the potential to distinguish benign from malignant small breast lesions when used with an MLP model. Prospective validation is needed before the model can influence clinical decision-making.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.