Preoperative Prediction of Recurrence Risk in Breast Cancer Patients Based on MRI Features

Motivation: Early prediction of recurrence risk is essential to treatment decision-making for breast cancer patients. Goal(s): To explore potential predictors of recurrence risk based on MRI features and to construct a preoperatively predictive model of risk. Approach: MRI features of 588 patients were investigated, 397 in training and 191 in testing data. Four machine learning methods were used to construct the predictive model. Results: Multiple lesions, irregular shape, spiculated margin, and peritumor edema were identified as predictive factors and used to construct the model. SVM showed the best predictive performance with AUC 0.87 (95%CI 0.83-0.91) and 0.73 (95%CI 0.75-0.81) in training and testing data. Impact: A preoperative predictive model based on MRI features could be a valuable tool for predicting recurrence risk and assisting in the personalized treatment of breast cancer patients.