Related Experiment Video For breast cancer
Updated: Jan 8, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Identification of NPM and non-mass breast cancer based on radiological features and radiomics
Zhen Guo1, Zhuonan Wang1,2, Xin Lin3
1Medical Imaging Center, Northwest Women's and Children's Hospital, Xi'an, China.
Background:
Non-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.
Purpose:
This 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.
Methods:
Mammographic 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).
Results:
Calcification 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).
Conclusions:
Radiomics 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.
