Objective To investigate the value of multimodal radiomics nomogram model based on mammography (MG) double body position combined with MRI double sequence in preoperative prediction of triple-negative breast cancer(TNBC).
Methods The clinicopathological, MG and MRI imaging data of 147 patients with breast cancer were analyzed, and randomly divided into the training set(n=102) and test set(n=45) according to the ratio of 7∶3.The regions of interest(ROI) were delineated on the cephalic and caudal MG(CC), internal and external oblique(MLO) and T2WI and DCE-MRI sequences of MRI in all patients.After the minimum-maximum normalization, the best features with high correlation with TNBC by Select K Best and LASSO were selected.Logistic regression(LR) and support vector machine(SVM) were used to establish the multimodal radiomics model based on MG and MRI, and the radiomics scores was obtained.The independent risk factors of clinical, MG and MRI image features were obtained by single and multiple logistic regression to construct clinical models.Finally, a multimodal radiomics nomogram model was constructed based on the clinical and imageomics risk factors screened by Rad-score.The area under receiver operating characteristic (AUC) curve was used to evaluate the predictive efficacy of model, and the calibration curve and decision curve were used to evaluate the stability and clinical practicability of model.
Results The efficiency of multimodal radiomics nomogram model in predicting TNBC was the best.The AUC, sensitivity, specificity and accuracy of training set were 0.957, 90.9%, 97.5% and 94.1%, respectively, and the AUC, sensitivity, specificity and accuracy of test set were 0.923, 88.9%, 91.7% and 86.7%, respectively.
Conclusions The multimodal radiomics nomogram model based on MG double body position and MRI double sequence can better and noninvasively predict TNBC before surgery.
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