Keywords: Breast, Breast, Machine Learning
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.
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