Potential of Radiomics to Improve Diagnostic Accuracy of Mammograms and Personalise Patient Management in Breast Cancer

Background: Breast cancer is one of the leading causes of female mortality, especially if diagnosed in late stages. While mammography is the cornerstone of screening, its diagnostic accuracy is limited by tumor heterogeneity and subjective interpretation. Objective: Herein, we explored the potential of radiomics and machine learning to improve the diagnostic accuracy of mammograms and personalise patient management in breast cancer. Methods: We manually segmented tumours and lymph nodes to analyse mammograms of the open-source INbreast dataset, which comprised multiple cases of benign and malignant breast masses with and without lymphadenopathy. Ra- diomics features (morphological, texture, wavelet) were extracted using PyRadiomics. Stratified sampling ensured balanced class representation. Then, we trained ML classifiers (XGBoost, CatBoost, LightGBM, etc.) to detect malignancy from the extracted radiomical features. Random Forest classifier was used to prognosticate the molecular subtype of the tumour from radiomical findings. Results: Significant radiomic differences were observed between benign and malignant lesions. Combining features of breast mass and lymph node yielded the highest classification accuracy (up to 99%) in detecting malignancy. The Random Forest model achieved 90.8% accuracy in identifying Luminal A molecular subtypes, with first-order and shape-based features contributing most to model perfor- mance. Conclusion: Radiomics-based ML models significantly improve diagnostic accuracy and enable non-invasive prediction of breast cancer subtypes. This approach supports precision oncology by enhancing screening efficiency and informing personalized treatment strategies.