Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma
Abstract The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' surv...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-40780-8 |
| _version_ | 1797453175224008704 |
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| author | Kaijiong Zhang Bo Ye Lichun Wu Sujiao Ni Yang Li Qifeng Wang Peng Zhang Dongsheng Wang |
| author_facet | Kaijiong Zhang Bo Ye Lichun Wu Sujiao Ni Yang Li Qifeng Wang Peng Zhang Dongsheng Wang |
| author_sort | Kaijiong Zhang |
| collection | DOAJ |
| description | Abstract The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients’ survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies. |
| first_indexed | 2024-03-09T15:19:03Z |
| format | Article |
| id | doaj.art-f301e1a24ef24c7b90a93dcfb8418f4e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:19:03Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f301e1a24ef24c7b90a93dcfb8418f4e2023-11-26T12:55:35ZengNature PortfolioScientific Reports2045-23222023-08-0113111210.1038/s41598-023-40780-8Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinomaKaijiong Zhang0Bo Ye1Lichun Wu2Sujiao Ni3Yang Li4Qifeng Wang5Peng Zhang6Dongsheng Wang7Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaAbstract The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients’ survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.https://doi.org/10.1038/s41598-023-40780-8 |
| spellingShingle | Kaijiong Zhang Bo Ye Lichun Wu Sujiao Ni Yang Li Qifeng Wang Peng Zhang Dongsheng Wang Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma Scientific Reports |
| title | Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma |
| title_full | Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma |
| title_fullStr | Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma |
| title_full_unstemmed | Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma |
| title_short | Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma |
| title_sort | machine learning based prediction of survival prognosis in esophageal squamous cell carcinoma |
| url | https://doi.org/10.1038/s41598-023-40780-8 |
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