Objective This study aims to predict pathological complete response(pCR)to neoadjuvant therapy(NAT)in breast cancer using pre-treatmentDCE-MRI intratumoral and peritumoral radiomics features combined with clini-cal-pathological characteristics.Additionally,it visualizes and analyzes the model using the Shapley Additive Explanation(Shapley)algorithm.Methods Clinical data of 656 patientswith breast cancer who received NAT and surgery from two hospitals were retrospectively analyzed,including 389 patients in the training group,166 patients in the internal validation group,and 101 patients in the external validation group.Radiomics features were extracted and selected based on the volume of interest(VOI)from DCE-MRI intratumoral and peritumoral regions,and the intratumoral,peritumoral and intratumoral combined peritumoral model were constructed,respectively.A clinical model was built through univariate and multivariate Logistic regression analysis.Finally,the combined model was constructed by integrating clinicaland pathological independent predictors and intratumoral combined peritumoral radiomics features.The performance of the model was evaluated using re-ceiver operating characteristic curves.The Shapley algorithm was employed to enhance model interpretability.Results Based on intratumoral and peritumoral VOI,the 16 and 5 best radiomics features were screened and the corresponding mod-els were constructed respectively.After a combined screening of bi-regional radiomic features,15 optimal radiomic features were retained to construct the intratumoral combined peri tumoral model.Clinical T-stage,HER2 status and molecular sub-types were identified as independent predictors for predicting NAT efficacy in breast cancer.The areas under the curve of the combined model in the training group,internal validation group and external validation group were 0.849(95％CI:0.811-0.886),0.819(95％CI:0.754-0.881)and 0.864(95％CI:0.789-0.928),respectively,which were higher than that of the clinical model,the intratumoral model,peritumoral model and intratumoral combined peritumoral model,and the differences were all statistically significant(all P＜0.05).Shapley analysis revealed that radscore was the most impor-tant feature in the model.Conclusion The combined clinical imaging model constructed by combining intratumoral and peritumoral radiomics features and clinic-pathological information could effectively predict the efficacy of NAT treatment for breast cancer.The Shapley algorithm can provide individual-level interpretability and guarantee the utility of the model.
维普
万方数据
