浙江大学学报（医学版）2026，Vol.55Issue(1)：46-55,10.DOI:10.3724/zdxbyxb-2025-0648
多模态影像组学模型预测临床淋巴结阴性甲状腺乳头状微小癌患者颈部中央区淋巴结转移
Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning
摘要
Abstract
Objective:To develop and validate a machine learning-based multimodal radiomics model for predicting central lymph node metastasis(CLNM)in patients with clinically node-negative(cN0)papillary thyroid microcarcinoma(PTMC).Methods:A retrospective study was conducted on the clinical data of 532 consecutive cN0 PTMC patients who underwent surgery at the Department of Thyroid Surgery of the First People's Hospital of Changzhou and the Department of Thyroid and Breast Surgery of Suzhou Municipal Hospital between January 2022 and June 2024.Among them,487 patients from the First People's Hospital of Changzhou were randomly assigned to a training set(n=352)or an internal validation set(n=135),while 45 patients from Suzhou Municipal Hospital served as an external validation set.Clinical feature screening involved collinearity analysis using variance inflation factors,followed by logistic regression to identify independent risk factors for CLNM.Radiomics features were extracted from ultrasound and CT images.An initial feature screening was performed using statistical tests(t-test or Mann-Whitney U test,P<0.05)along with mutual information analysis(score>0.015),followed by least absolute shrinkage and selection operator(LASSO)regression for key feature selection.Using the optimized feature set,four machine learning models were constructed:random forest,gradient boosting machine(GBM),support vector machine,and K-nearest neighbors.Model performance was evaluated using the area under the receiver operating characteristic curve(AUC),decision curve analysis,and Shapley Additive exPlanations(SHAP)method.Results:Logistic regression identified five clinical features independently associated with CLNM:age<55 years(OR=2.391,95%CI:1.072-5.334,P<0.05),coexisting Hashimoto's thyroiditis(OR=3.084,95%CI:1.474-6.453,P<0.01),maximum tumor diameter(OR=11.086,95%CI:2.881-48.378,P<0.01),monocyte count(OR=0.005,95%CI:0.001-0.044,P<0.01),and the lymphocyte-to-monocyte ratio(OR=0.564,95%CI:0.486-0.654,P<0.01).LASSO regression selected two key ultrasound and six key CT radiomics features.Among the four models,the GBM model based on multimodal feature fusion performed best,with AUC values of 0.975,0.833,and 0.916,accuracies of 0.925,0.748,and 0.863,specificities of 0.950,0.800,and 0.881,and sensitivities of 0.900,0.720,and 0.804 in the training,internal validation,and external validation sets,respectively.Decision curve analysis showed that the GBM model provided the highest net clinical benefit within the threshold probability range of 0.1-0.8.SHAP feature importance analysis revealed that the lymphocyte-to-monocyte ratio and monocyte count contributed most to CLNM prediction,followed by maximum tumor diameter and radiomics texture features.Conclusion:The GBM-based multimodal radiomics model can accurately predict the risk of CLNM in patients with cN0 PTMC,which may facilitate individualized preoperative risk stratification and clinical descision-making.关键词
甲状腺乳头状癌/甲状腺微小癌/中央区淋巴结转移/影像组学/预测模型/机器学习Key words
Papillary thyroid carcinoma/Microcarcinoma of thyroid/Central lymph node metastasis/Radiomics/Forecasting model/Machine learning分类
医药卫生引用本文复制引用
冯嘉伟,杨语欣,刘水清,秦安成,叶晶,江勇..多模态影像组学模型预测临床淋巴结阴性甲状腺乳头状微小癌患者颈部中央区淋巴结转移[J].浙江大学学报（医学版）,2026,55(1):46-55,10.基金项目
常州市龙城英才计划-青年科技人才托举工程(常科协[2023]52号) （常科协[2023]52号）
常州市第十一批科技计划(CJ20244009)This study was supported by Changzhou Longcheng Talent Program-Young Scientific and Technological Talent Support Project (Changzhou Association for Science and Technology[2023]No. 52) and the 11th Batch of Changzhou Science and Technology Program (CJ20244009) （CJ20244009）
