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| A prediction model for clinical staging of lung cancer based on tongue image parameters and traditional Chinese medicine clinical symptoms of patients |
| Hits 25 Download times 8 Received:December 28, 2025 |
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| DOI
10.11656/j.issn.1672-1519.2026.05.05 |
| Key Words
lung cancer;tongue image;prediction model;Logistic regression;machine learning |
| Author Name | Affiliation | E-mail | | WANG Dongjun | School of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan 063210, China
School of Public Health and Health Sciences, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | | | WEI Kai | School of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan 063210, China | | | TIAN Zhikui | School of Rehabilitation Qilu Medical University, Zibo 255300, China | | | SUN Xuan | School of Traditional Chinese Medicine, Shandong Medical and Pharmaceutical University, Yantai 264003, China | | | ZHANG Ying | Department of Internal Medicine Ⅴ, Fengnan District Hospital of Traditional Chinese Medicine of Tangshan City, Tangshan 063000, China | 1057226313@qq.com | | WANG Hongwu | School of Public Health and Health Sciences, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | |
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| Abstract
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| [Objective] To construct a clinical stage prediction model of lung cancer based on the basic information of lung cancer patients，tongue image parameters and traditional Chinese medicine（TCM） symptoms. [Methods] With reference to the cross-sectional investigation and research method of clinical epidemiology，the macro and micro characteristics of lung cancer tongue image and related influencing factors were explored. Independent variables were screened for tongue image parameters of lung cancer patients and TCM symptoms questionnaire，and statistically significant variables were included in binary Logistic regression analysis. Logistic regression，support vector machine，random forest，extreme gradient lifting，K-nearest neighbor classification algorithm and backpropagation neural network machine learning intelligent algorithm were used to evaluate the predictive ability of lung cancer clinical stage model. [Results] Correlation analysis results showed that 20 variables were correlated with the clinical stage progression of lung cancer through univariate analysis. They were age（OR=1.618，P<0.001），KPS score（OR=2.416，P<0.001），medical history（OR=2.104，P<0.05），smoking history（OR=2.275，P<0.001），drinking history（OR=1.357，P<0.05），and course of disease（OR=1.257，P<0.001），radiotherapy（OR=0.631，P<0.001），CC-B5（OR=1.807，P<0.001），greasy coating（OR=1.612，P<0.001），cracked tongue（OR=1.988，P<0.05），spontaneous sweating（OR=1.775，P<0.05）Blurred vision（OR=1.495，P<0.001），dry mouth（OR=1.691，P<0.001），dry cough with little sputum（OR=1.443，P<0.01），chest pain（OR=1.849，P<0.05），oligopsia（OR=1.561，P<0.05），dull complexion（OR=2.081，P<0.001），pale lip color（OR=1.184，P<0.05） skin onychia（OR=1.299，P<0.05），wheezing（OR=1.194，P<0.05）. The ROC curve was drawn with the clinical stage of lung cancer as the dependent variable and the prediction probability of the discriminant model as the independent variable. The area under the ROC curve of the Logistic regression model was 0.946. In Logistic regression model，the AUC of stages Ⅰ to Ⅲ were 0.901，0.960，0.953 and 0.971，respectively.The AUC area of Logistic regression prediction probability was 0.946，95%CI（0.877，0.973）. The AUC area of RF algorithm prediction probability is 0.945，95%CI（0.802，0.977）. The AUC area of SVM prediction probability is 0.942，95%CI（0.864，0.952） and XGboost prediction probability is 0.931，95%CI（0.814，0.948） and BP neural network prediction probability is 0.930.The AUC area of 95%CI（0.793，0.965） and KNN prediction probability is 0.927，95%CI（0.775，0.946）. [Conclusion] Based on patients’ basic information，tongue image parameters and traditional Chinese medicine symptoms，it is feasible to construct lung cancer clinical stage prediction model by using Logistic regression and machine learning methods，which has good prediction ability and classification efficiency，and has clinical value of promoting auxiliary diagnosis and treatment，judging prognosis and risk early warning. |
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