MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer
ABSTRACT Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This study collected clinicopathological va...
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| Format: | Article |
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Wiley
2025-04-01
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| Series: | Clinical and Translational Science |
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| Online Access: | https://doi.org/10.1111/cts.70186 |
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| author | Jie Lan Heng Wang Jing Huang Weiyi Li Min Ao Wanfeng Zhang Junhao Mu Li Yang Longke Ran |
| author_facet | Jie Lan Heng Wang Jing Huang Weiyi Li Min Ao Wanfeng Zhang Junhao Mu Li Yang Longke Ran |
| author_sort | Jie Lan |
| collection | DOAJ |
| description | ABSTRACT Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This study collected clinicopathological variables from the pulmonary nodule and lung cancer database of the First Affiliated Hospital of Chongqing Medical University, where patients with early‐stage (cT1) solitary lung cancer were evaluated from 2018.11 to 2022.10. The random forest model and Shapley Additive Explanations (SHAP) were used to investigate the importance of clinical features in the feature selection part. Random Forest, Gradient Boosting, and AdaBoost classifiers were applied to build the final model, and the predictive discrimination of each model was compared based on the receiver operating characteristics (ROC) curve and precision and recall curve. With the evaluation of feature importance, 9 features were used to construct the prediction model finally. The Random Forest model yielded an average precision of 0.93 with an area under the curve (AUC) of 0.92 (95% CI: 0.88–0.94) compared with the Gradient Boosting and AdaBoost classifiers in the internal validation dataset, yielding an average precision of 0.87 and 0.91 with AUCs of 0.87 (95% CI: 0.84–0.93) and 0.90 (95% CI: 0.86–0.92), respectively. In addition, the Random Forest classifier performed best in 5 other 5 diagnostic indices. Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user‐friendly tool assisting in the metastasis prediction of cT1 solid lung cancer. |
| format | Article |
| id | doaj-art-e974711966054ba1aeb303529b89bd8f |
| institution | OA Journals |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-e974711966054ba1aeb303529b89bd8f2025-08-20T02:25:08ZengWileyClinical and Translational Science1752-80541752-80622025-04-01184n/an/a10.1111/cts.70186MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung CancerJie Lan0Heng Wang1Jing Huang2Weiyi Li3Min Ao4Wanfeng Zhang5Junhao Mu6Li Yang7Longke Ran8Department of Bioinformatics The Basic Medical School of Chongqing Medical University Chongqing ChinaDepartment of Bioinformatics The Basic Medical School of Chongqing Medical University Chongqing ChinaDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Chongqing Medical University Chongqing ChinaDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Chongqing Medical University Chongqing ChinaDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Chongqing Medical University Chongqing ChinaDepartment of Bioinformatics The Basic Medical School of Chongqing Medical University Chongqing ChinaDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Chongqing Medical University Chongqing ChinaDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Chongqing Medical University Chongqing ChinaDepartment of Bioinformatics The Basic Medical School of Chongqing Medical University Chongqing ChinaABSTRACT Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This study collected clinicopathological variables from the pulmonary nodule and lung cancer database of the First Affiliated Hospital of Chongqing Medical University, where patients with early‐stage (cT1) solitary lung cancer were evaluated from 2018.11 to 2022.10. The random forest model and Shapley Additive Explanations (SHAP) were used to investigate the importance of clinical features in the feature selection part. Random Forest, Gradient Boosting, and AdaBoost classifiers were applied to build the final model, and the predictive discrimination of each model was compared based on the receiver operating characteristics (ROC) curve and precision and recall curve. With the evaluation of feature importance, 9 features were used to construct the prediction model finally. The Random Forest model yielded an average precision of 0.93 with an area under the curve (AUC) of 0.92 (95% CI: 0.88–0.94) compared with the Gradient Boosting and AdaBoost classifiers in the internal validation dataset, yielding an average precision of 0.87 and 0.91 with AUCs of 0.87 (95% CI: 0.84–0.93) and 0.90 (95% CI: 0.86–0.92), respectively. In addition, the Random Forest classifier performed best in 5 other 5 diagnostic indices. Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user‐friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.https://doi.org/10.1111/cts.70186cT1 solid lung cancermachine learningprediction modelpulmonary nodulesrandom forest |
| spellingShingle | Jie Lan Heng Wang Jing Huang Weiyi Li Min Ao Wanfeng Zhang Junhao Mu Li Yang Longke Ran MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer Clinical and Translational Science cT1 solid lung cancer machine learning prediction model pulmonary nodules random forest |
| title | MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer |
| title_full | MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer |
| title_fullStr | MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer |
| title_full_unstemmed | MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer |
| title_short | MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer |
| title_sort | molpre a machine learning model to predict metastasis of ct1 solid lung cancer |
| topic | cT1 solid lung cancer machine learning prediction model pulmonary nodules random forest |
| url | https://doi.org/10.1111/cts.70186 |
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