Machine Learning with Multiparametric MRI for preoperative prediction of intraductal component in invasive breast cancer

Abstract

Motivation: To predict the presence of an intraductal component (ductal carcinoma in situ, DCIS) in invasive breast cancer (IBC-IC). Goal(s): To improve the preoperative prediction of IBC-IC. Approach: This study was to develop and validate a machine-learning algorithm to preoperatively predict IBC-IC using the multiparametric MRI features. Results: The machine learning model with multiparametric MRI features could provide the individualized probability of IBC-IC and might help to optimize surgical planning for patients with breast cancer before BCS. Impact: This study developed a prediction model combining a machine-learning algorithm with multiparametric MRI features to preoperatively predict intraductal component in invasive breast cancer, which may be beneficial to the preoperative planning of breast-conserving surgery for early-stage invasive breast cancer.