AUTHOR=Guo Zhen , Wang Zhuonan , Lin Xin , Chen Xingyu , Liu Wei , Yan Rui 

TITLE=Identification of NPM and non-mass breast cancer based on radiological features and radiomics

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1665427

DOI=10.3389/fonc.2025.1665427

ISSN=2234-943X

ABSTRACT=BackgroundNon-mass breast cancer, presenting with calcifications, asymmetric dense shadows, and architectural distortions, is challenging to distinguish from non-puerperal mastitis (NPM) due to radiological similarities on mammography.PurposeThis study aims to develop a mammographic-based radiomics model to differentiate NPM from non-mass breast cancer, addressing the limitations of subjective BI-RADS assessments that risk misdiagnosis or delayed treatment.MethodsMammographic images from 104 patients (44 NPM, 60 non-mass breast cancer), collected from January 2018 to June 2023, were retrospectively analyzed. Two senior breast radiologists independently reviewed images, with disagreements resolved by a more senior radiologist. Regions of interest (ROIs) were manually delineated using 3DSlicer, and 576 radiomic features (shape, first-order, texture) were extracted using PyRadiomics. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 10-fold nested cross-validation selected 6 predictive features, and a support vector machine (SVM) model with a Radial Basis Function kernel was constructed. Performance was evaluated using nested cross-validation, calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsCalcification type and asymmetric dense shadows differed significantly between NPM and non-mass breast cancer (P < 0.05). The radiomics model achieved an AUC of 0.844 (95% CI: 0.787–0.904), accuracy of 0.769 (95% CI: 0.735–0.803), sensitivity of 0.883 (95% CI: 0.792–0.974), specificity of 0.678 (95% CI: 0.576–0.779), PPV of 0.784 (95% CI: 0.749–0.819), and NPV of 0.778 (95% CI: 0.662–0.896), compared with radiologists’ BI-RADS assessment (AUC: 0.860, 95% CI: 0.790–0.930; accuracy: 0.856, 95% CI: 0.787–0.923; sensitivity: 0.833, 95% CI: 0.736–0.926; specificity: 0.886, 95% CI: 0.791–0.979; PPV: 0.909, 95% CI: 0.832–0.984; NPV: 0.796, 95% CI: 0.679–0.907).ConclusionsRadiomics using PyRadiomics-extracted features, LASSO, and SVM provides a robust quantitative tool to differentiate NPM from non-mass breast cancer, enhancing diagnostic precision and clinical decision-making.