基于超声深度学习影像组学的乳腺癌新辅助化疗疗效预测OACSTPCD

Objective To develop a comprehensive model combining ultrasound radiomics,deep learning,and clinical features to predict the pathological complete response(pCR)after neoadjuvant chemotherapy(NAC)for breast cancer.Methods A total of 117 patients with breast cancer were included,and the training set and validation set were randomly divided according to a ratio of 7∶3.Mann-Whitney U test,random forest recursive feature elimination,and least absolute shrinkage and selection operators were used for feature screening and radiomics/deep learning signature construction.Single/multi-factor analysis of patients'clinical parameters were performed to select effective features to construct clinical models.Then Logistic regression algorithm was used to combine clinical features with radiomics and deep learning signatures to construct a clinical-radiomics-deep learning comprehensive model.Model performance was evaluated in terms of predictive efficacy,calibration ability,and clinical utility.Results The clinical-radiomics-deep learning comprehensive model showed the highest areas under curve compared to the separate clinical,radiomics,and deep learning models in both the training and validation sets(0.949 vs.0.788 vs.0.815 vs.0.928 for the training set;0.931 vs.0.643 vs.0.778 vs.0.901 for the validation set).The calibration curves and decision curves confirmed the good predictive performance of the comprehensive model.Conclusion Compared with the single model,the comprehensive model has a higher value in predicting the pCR status of breast cancer patients after NAC before surgery.