实用医学杂志2026，Vol.42Issue(9)：1501-1510,10.DOI:10.3969/j.issn.1006-5725.2026.09.003
基于深度学习超声模型对乳腺癌分子分型与新辅助治疗早期最佳疗效时机的预测
Predicting molecular subtyping and optimal early response assessment in breast cancer neoadjuvant therapy:A deep learning ultrasound approach
摘要
Abstract
Objective This study aimed to develop a longitudinal deep learning ultrasound model to achieve two objectives:non-invasive prediction of breast cancer molecular subtypes prior to neoadjuvant therapy(NAT)and identification of the optimal timing for early efficacy assessment during NAT.Methods We enrolled 176 breast cancer patients from Guangzhou First People's Hospital who completed the full NAT course.The cohort was stratified into two analysis subsets:176 patients for molecular subtyping and 167 for treatment response evalua-tion.Pathological data and serial ultrasound images were collected.Tumors were categorized into four molecular subtypes via immunohistochemistry.Treatment response was classified as"significant"or"non-significant"based on postoperative Miller-Payne grading.We employed a hybrid U-Net-EfficientNet-B0 architecture integrated with a segmentation-guided attention mechanism(SegAttend-Net).The model leveraged pre-treatment images and dy-namic sonographic feature changes across early NAT stages to predict molecular subtypes and therapeutic response.Confidence intervals were calculated using Clopper-Pearson exact and Bootstrap methods.Performance metrics across treatment cycles were adjusted for multiple comparisons using the Benjamini-Hochberg procedure.Evalua-tion utilized confusion matrices and longitudinal performance trajectories.Results In molecular subtyping,the model achieved accuracies of 82％(Luminal A),88％(Luminal B),72％(HER2-overexpressing),and 96％(triple-negative).For efficacy prediction,overall accuracy increased from 71％at cycle 1 to 80％at cycle 4,while sensitivity improved markedly from 0.14 to 0.79.The sensitivity improvement between the 3rd and 4th cycles was statistically significant.Conclusions The developed SegAttend-Net model demonstrates efficacy in pre-NAT molecu-lar subtyping and holds clinical value for early efficacy assessment,with optimal predictive performance observed at the fourth treatment cycle.关键词
超声预测模型/乳腺癌/新辅助治疗/分子分型/疗效预测Key words
ultrasound prediction model/breast cancer/neoadjuvant therapy/molecular subtyp-ing/treatment efficacy prediction分类
医药卫生引用本文复制引用
罗为尧,范誉铧,黎一夫,陈娟,邓勇杰,柳建华,胡志文,马穗红..基于深度学习超声模型对乳腺癌分子分型与新辅助治疗早期最佳疗效时机的预测[J].实用医学杂志,2026,42(9):1501-1510,10.基金项目
国家自然科学基金项目(编号:82071935) （编号:82071935）
