Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study
Abstract 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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02266-5 |
| _version_ | 1797452412819079168 |
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| author | Yulin Shi Hao Wang Xinghua Yao Jun Li Jiayi Liu Yuan Chen Lingshuang Liu Jiatuo Xu |
| author_facet | Yulin Shi Hao Wang Xinghua Yao Jun Li Jiayi Liu Yuan Chen Lingshuang Liu Jiatuo Xu |
| author_sort | Yulin Shi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T15:08:21Z |
| format | Article |
| id | doaj.art-eaa85d33c05149fa951ad251852fea22 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-09T15:08:21Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-eaa85d33c05149fa951ad251852fea222023-11-26T13:32:22ZengBMCBMC Medical Informatics and Decision Making1472-69472023-09-0123111410.1186/s12911-023-02266-5Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot studyYulin Shi0Hao Wang1Xinghua Yao2Jun Li3Jiayi Liu4Yuan Chen5Lingshuang Liu6Jiatuo Xu7The Office of Academic AffairsCollege of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineCollege of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineCollege of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineCollege of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineLonghua Hospital, Shanghai University of Traditional Chinese MedicineLonghua Hospital, Shanghai University of Traditional Chinese MedicineCollege of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineAbstract 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.https://doi.org/10.1186/s12911-023-02266-5Non-small cell lung cancer (NSCLC)Clinical stagesTongue diagnosisTumor markerPrediction model |
| spellingShingle | Yulin Shi Hao Wang Xinghua Yao Jun Li Jiayi Liu Yuan Chen Lingshuang Liu Jiatuo Xu Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study BMC Medical Informatics and Decision Making Non-small cell lung cancer (NSCLC) Clinical stages Tongue diagnosis Tumor marker Prediction model |
| title | Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study |
| title_full | Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study |
| title_fullStr | Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study |
| title_full_unstemmed | Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study |
| title_short | Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study |
| title_sort | machine learning prediction models for different stages of non small cell lung cancer based on tongue and tumor marker a pilot study |
| topic | Non-small cell lung cancer (NSCLC) Clinical stages Tongue diagnosis Tumor marker Prediction model |
| url | https://doi.org/10.1186/s12911-023-02266-5 |
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