Objective To construct an interpretable prediction model for triple negative breast cancer patients,which can simultaneously achieve good prediction and interpretation capabilities.Methods The clinical features and multi sequence and multi parameter MRI images of 136 patients with breast cancer were retrospectively analyzed,including 23 cases of triple negative breast cancer and 113 cases of non triple negative breast cancer.After screening and constructing the model by sketching and extracting the radiomic features,the machine learning framework was constructed by combining the radiomic features and independent clinical image features.In addition,the SHAP(Sharpley Additive exPlanning)model interpreter was used to provide personalized evaluation and interpretation to achieve personalized clinical decision support.Results After screening the omics features,11 radiomic features were involved in the calculation of the radiomic score,and their AUC in the training set and the test set were 0.898 and 0.803.The prediction accuracy was further improved by combining with the clinical model.Conclusion The multimodal interpretable prediction model may help clinicians identify triple negative breast cancer risk patients more accurately and quickly,and provide timely and accurate treatment for patients.
Triple negative breast cancerMRIRadiomicsSHAP algorithm
NETL
NSTL
万方数据
