Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms

ConclusionsThis study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists.Key Points•Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes.•The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs.•Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Source: European Radiology - October 13, 2021 Category: Radiology Source Type: research