实用医学杂志2026，Vol.42Issue(1)：45-55,11.DOI:10.3969/j.issn.1006-5725.2026.01.006
可解释反向传播神经网络在预测前哨淋巴结1～2枚阳性乳腺癌患者腋窝淋巴结负荷中的价值
Predictive performance of an interpretable BPNN model for axillary lymph node burden in breast cancer patients with 1～2 sentinel lymph node positive
摘要
Abstract
Objective To evaluate the accuracy of a backpropagation neural network(BPNN)model incorporating clinical and imaging features in predicting axillary lymph node burden among breast cancer patients with one to two positive sentinel lymph nodes.Methods We retrospectively analyzed clinical and imaging data from 386 female breast cancer patients who underwent axillary lymph node dissection at three medical centers between January 2021 and December 2024.Based on pathological findings,patients were categorized into a high axillary lymph node burden group(n=155)and a low burden group(n=231).Data from Center 1 and Center 2(Affiliated Hospital of Guangdong Medical University and Affiliated Yangjiang Hospital of Guangdong Medical University;n=295)were randomly divided into a training set(n=207)and an internal validation set(n=88),while data from Center 3(Second Affiliated Hospital of Guangdong Medical University;n=91)served as the exter-nal validation cohort.Univariate and multivariate logistic regression analyses were conducted in the training cohort to identify independent risk factors.Four machine learning algorithms,including logistic regression,support vector machine(SVM),random forest and BPNN were then used to construct predictive models,which were subse-quently evaluated in the internal and external validation cohorts.The Shapley additive explanation(SHAP)method was applied to assess and visualize feature importance.Results Univariate and multivariate logistic regression analyses identified the neutrophil-to-lymphocyte ratio(NLR),peritumoral edema,and axillary lymph node corti-cal thickening as independent predictors of nodal burden.The BPNN-based model demonstrated superior predictive performance,achieving an area under the receiver operating characteristic curve(AUC)of 0.793.SHAP analysis revealed that peritumoral edema contributed most significantly to model predictions,followed by lymph node cortical thickening and NLR.Conclusions An interpretable BPNN model integrating clinical and imaging characteristics provides reliable prediction of axillary lymph node burden in breast cancer patients.This approach offers valuable support for axillary management and personalized treatment planning.关键词
乳腺癌/腋窝淋巴结负荷/前哨淋巴结1～2枚阳性/反向传播神经网络/可解释性Key words
breast cancer/axillary lymph node burden/1～2 sentinel lymph node positivity/back propagation neural network/interpretability分类
医药卫生引用本文复制引用
农盛,李湛雄,张琪,卢振东,洪敏萍,陈武标,刘子霖..可解释反向传播神经网络在预测前哨淋巴结1～2枚阳性乳腺癌患者腋窝淋巴结负荷中的价值[J].实用医学杂志,2026,42(1):45-55,11.基金项目
浙江省医药卫生科技计划项目(编号:2023KY338) （编号:2023KY338）
