BMC cancer
Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer
By Reisig E, Cai L, Müller M and 2 others.
Abstract
Background
Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and patient characteristics.
Methods
Clinical data from 388 cases of invasive breast cancer receiving neoadjuvant treatment were extracted in this retrospective, monocentric study. Findings from ultrasound, mammography and magnetic resonance imaging combined with patient and histopathological information were included. A Pearson Product-Moment correlation and a LASSO regularization were performed to preselect the most relevant features for the outcome. Based on the preselected variables, we developed and validated a machine learning algorithm (logistic regression with elastic net penalty (GLM)) to predict pCR. The model's performance was evaluated via area under the curve (AUROC), false positive rate (FPR) and positive predictive value (PPV).