The nomogram model can effectively predict triple-negative breast cancer:based on the combination of ultrasound intratumor,peritumor radiomics and clinical ultrasound features

ZOU Qiao; LIU Jinhui; LENG Xiaoling; ZUMURETI·Tuerhong; FAN Xiwen

doi:10.12122/j.issn.1674-4500.2025.01.01

Abstract：Objective To evaluate the predictive value of the nomogram model constructed based on the combination of ultrasound intratumor and peritumor radiomics and clinical ultrasound features in triple-negative breast cancer(TNBC).Methods The relevant data of 540 patients with pathologically confirmed invasive breast cancer in the Affiliated Cancer Hospital of Xinjiang Medical University were retrospectively collected from January 2018 to December 2023,and divided into the TNBC group(n=263)and the non-TNBC group(n=277)according to the results of immunohistochemistry,and randomly divided into the training set(n=378)and the validation set(n=162)according to the 7:3 randomization.The radiomics features were extracted using Pyradiomics in the intratumoral and peritumoral ROIs of the original and derived images;the features were screened by Pearson's correlation coefficient,maximal correlation minimal redundancy and LASSO.The intratumoral,peritumoral,and intratumoral+peritumoral models were established and radiomics signature were obtained,respectively,and after univariate and multivariate Logistic regression analysis was used to screen the independent predictors of TNBC from clinical ultrasound features and construct the clinical model.The radiomics signature with the best efficacy and the clinical independent predictors were selected to construct the nomogram model.ROC curves,calibration curves and clinical decision curves were plotted to evaluate the model efficacy,and the differences in the area under the curve(AUC)of the models were compared by the Delong test.Results The models were finally constructed by intratumoral(n=14),peritumoral(n=15),and intratumoral+peritumoral(n=19)radiomics features,and the intratumoral+peritumoral radiomics model had the best predictive efficacy,with AUCs of 0.941 and 0.934 for the training and validation sets,respectively.The clinical models were constructed by using the tumor size,background echotexture,microcalcifications,and posterior echoes as independent predictors,and the AUCs of training and validation set were 0.906,0.911.Intratumor+peritumor radiomics signature combined with tumor size,background echotexture,microcalcification,and posterior echo were used to construct the nomogram model,and the training set and validation set AUC were improved to 0.974,0.965,respectively.Conclusion The nomogram model constructed based on the combination of ultrasound intratumor and peritumor radiomics and clinical ultrasound features has a higher prediction value for TNBC,which can provide reference for clinical decision-making.