Objective To investigate the prediction of human epidermal growth factor receptor 2(HER-2)expression level in breast cancer patients based on the nomogram constructed by multimodal radiomics combined with clinical features.Methods 201 patients with pathologically confirmed breast cancer who underwent magnetic resonance imaging(MRI)and mammogram(MG)examination before surgery in the First Affiliated Hospital of Bengbu Medical University were analyzed retrospectively,and randomly assigned to the training set(n=140)and test set(n=61)at a ratio of 7:3.MG craniocau-dal(CC)position,Mediolateral oblique(MLO)position,T2 WI fat inhibition(FS-T2WI)sequence and DCE-MRI phase 2 were selected to delineate the maximum lesion level.The optimal features were selected by f-calssif function,least absolute shrinkage and selection operator(LASSO)regression,and mammogram(MG),dynamic contrast-enhanced MRI(DCE-MRI)and multimodal radiomics score(Rad-score)were obtained by support vector machine(SVM).MG model,MRI model and multi-modal model were constructed respectively.The independent clinical predictors were screened by single-multiple logistic regression to build the clinical model,and the multi-modal model Rad-score combined with independent clinical predictors was selected to build the nomogram model.Results The AUC,sensitivity,specificity and accuracy of the training set of nomogram model were 0.902,91.8％,85.7％and 85.0％,respectively.The test sets were 0.886,81.8％,84.6％and 80.3％,respectively.Conclusion The nomogram may be used as an accurate and non-invasive method to predict the expression level of HER-2 in preoperative breast cancer patients,and provide important guidance for clinical diagnosis and treatment and decision-making.
Breast cancerHER-2 expressionmultimodalRadiomicsnomogram
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NSTL
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