基于舌象和肿瘤标志物的非小细胞肺癌不同阶段的机器学习预测模型：一项初步研究。

OBJECTIVE: To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. METHODS: Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. RESULTS: There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models' classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively. CONCLUSIONS: There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

目的：分析不同分期非小细胞肺癌（NSCLC）的舌象特征，以及舌象特征与肿瘤标志物的相关性，探讨基于舌象特征和肿瘤标志物建立不同分期 NSCLC 预测模型的可行性。

方法：收集非晚期 NSCLC 患者（n=109）和晚期 NSCLC 患者（n=110）的舌象图像，对舌象图像进行分析以获取舌象特征，并分析不同分期 NSCLC 中舌象特征与肿瘤标志物的相关性。在此基础上，采用决策树、逻辑回归、支持向量机、随机森林、朴素贝叶斯和神经网络等 6 种分类器，基于舌象特征和肿瘤标志物建立不同分期 NSCLC 的预测模型。

结果：非晚期和晚期 NSCLC 组之间的舌象特征存在统计学差异。晚期 NSCLC 组中舌象特征与肿瘤标志物之间具有统计学意义的指标数量明显高于非晚期 NSCLC 组，且相关性更强。在不同分期 NSCLC 的模型中，支持向量机（SVM）、决策树和逻辑回归等机器学习方法的性能较差。神经网络、随机森林和朴素贝叶斯对舌象特征和肿瘤标志物及基线数据的分类效率较高。模型的分类准确率分别为 0.767±0.081、0.718±0.062 和 0.688±0.070，AUC 分别为 0.793±0.086、0.779±0.075 和 0.771±0.072。

结论：不同分期 NSCLC 之间的舌象特征存在统计学差异，晚期 NSCLC 的舌象特征与肿瘤标志物的相关性更为密切。由于信息有限，在本研究中，单数据源包括基线、舌象特征和肿瘤标志物不能用于识别不同分期的 NSCLC。除了逻辑回归方法外，其他基于肿瘤标志物和基线数据集的机器学习方法，通过添加舌象图像数据，可有效提高不同分期 NSCLC 的鉴别诊断效率，这需要在未来的大样本研究中进一步验证。