ArticleBMC medical informatics and decision making2023
Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Pooled it
- Exploring traditional Chinese medicine tongue diagnosis in potential cancer identification: A scoping review.Integrative medicine research · 2026Review
- A preliminary study of a machine-learning prediction model for lung cancer diagnosis based on routine blood indicators and traditional chinese medicine diagnostic parameters.Frontiers in pharmacology · 2026Article
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Review
- Machine Learning for Prediction of Non-Small Cell Lung Cancer Based on Inflammatory and Nutritional Indicators in Adults: A Cross-Sectional Study.Cancer management and research · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsTongue 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.
resultsThere 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.
conclusionsThere 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.
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