中医肿瘤学杂志2026，Vol.8Issue(3)：37-52,16.DOI:10.19811/j.cnki.ISSN2096-6628.2026.05.006
基于舌象客观化的转移性结直肠癌多模态特征融合诊断模型构建
Construction of A Multimodal Feature Fusion Diagnostic Model for Metastatic Colorectal Cancer Based on Objective Tongue Diagnosis
摘要
Abstract
Objective This study aims to explore a non-invasive and convenient method for diagnosing metastatic colorectal cancer(mCRC)based on the objectification of tongue diagnosis and traditional Chinese medicine(TCM)characteristics.Methods A total of 282 colorectal cancer patients were enrolled in the study,including 151 cases of non-metastatic colorectal cancer and 131 cases of metastatic colorectal cancer.The ZK-S01 portable tongue diagnosis instrument was used to collect tongue images for objective analysis.Meanwhile,multidimensional information such as demographics,lifestyle,disease characteristics,pulse conditions,and TCM symptom scores was collected.Chi-square test or independent samples t-test was used to analyze the differences in tongue image features and other clinical parameters(lifestyle,disease characteristics,pulse conditions,TCM symptoms).Predictive factors for the model were screened with P＜0.05 as the inclusion criterion.Dimension reduction and re-screening of the predictive factors were performed using Spearman analysis,and the data after dimension reduction were used as the predictive factors for the final model.Nine common machine learning methods,including k-nearest neighbors,random forest,extreme gradient boosting(XGBoost),and Stacking,were adopted to construct a multi-dimensional fusion diagnostic model for mCRC.Eighty percent of the data was used as the training set,and 20%as the test set.Five-fold cross-validation was used to verify the model performance,and the area under the curve(AUC)and F1-score were used to evaluate the model performance.Results The key predictive factors for distinguishing between metastatic and non-metastatic colorectal cancer included tongue with ecchymoses,RAS/BRAF gene mutation,greasy tongue coating,pain,and slippery pulse.The multi-modal feature fusion model integrating various clinical information showed good accuracy and generalization ability,among which the Stacking algorithm achieved the optimal performance(F1-score:79.25%,AUC:80.55%,sensitivity:80.77%,specificity:80.33%,accuracy:80.53%,precision:82.10%).Conclusion The machine learning model integrating objective tongue image features and multidimensional clinical information can effectively distinguish between metastatic and non-metastatic colorectal cancer,providing a non-invasive and convenient staging diagnostic tool for clinical practice.关键词
结直肠癌分期/舌象客观化/多模态特征融合/机器学习/诊断模型Key words
colorectal cancer staging/objectification of tongue features/multimodal feature fusion/machine learning/diagnostic model分类
医药卫生引用本文复制引用
徐钰莹,杨宇飞,李芮,张继伟,张雪雪,宋婷婷,隗睿,许云,李秋艳..基于舌象客观化的转移性结直肠癌多模态特征融合诊断模型构建[J].中医肿瘤学杂志,2026,8(3):37-52,16.基金项目
首都卫生发展科研专项(编号:2022-1-4171) （编号:2022-1-4171）
中国中医科学院科技创新工程(编号:CI2021A05012). （编号:CI2021A05012）
