中国医疗设备2024，Vol.39Issue(4)：122-129,8.DOI:10.3969/j.issn.1674-1633.2024.04.021
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
Prediction of Efficacy of Neoadjuvant Chemotherapy for Breast Cancer Based on Ultrasound-Based Deep Learning Radiomics
摘要
Abstract
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.关键词
超声/影像组学/深度学习/乳腺癌/新辅助化疗/病理完全缓解Key words
ultrasound/radiomics/deep learning/breast cancer/neoadjuvant chemotherapy/pathological complete response分类
医药卫生引用本文复制引用
张恒,赵彤,张赛,孙佳伟,李晓琴,倪昕晔..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[J].中国医疗设备,2024,39(4):122-129,8.基金项目
国家自然科学基金面上项目(62371243) （62371243）
江苏省重点研发计划社会发展项目(BE2022720) （BE2022720）
江苏省卫健委医学科研立项面上项目(M2020006) （M2020006）
江苏省医学重点学科建设单位[肿瘤治疗学(放射治疗)](JSDW202237) （放射治疗）
江苏省自然基金面上项目(BK20231190) （BK20231190）
常州市社会发展项目(CE20235063). （CE20235063）
