Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress
Summary Background Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC). Methods This stud...
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BMC
2024-11-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-13115-7 |
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| author | Jin-Biao Xie Shi-Jie Huang Tian-Bao Yang Wu Wang Bo-Yang Chen Lianyi Guo |
| author_facet | Jin-Biao Xie Shi-Jie Huang Tian-Bao Yang Wu Wang Bo-Yang Chen Lianyi Guo |
| author_sort | Jin-Biao Xie |
| collection | DOAJ |
| description | Summary Background Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC). Methods This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots. Results The training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 − 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962–0.987), while the DT model is 0.784 (0.739–0.830) and the SVM is 0.879 (0.843–0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717–0.864), compared to the DT model with an AUC of 0.717 (0.640–0.794) and 0.779 (0.702–0.856) in SVM. Conclusions The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery. |
| format | Article |
| id | doaj-art-50abc76bbbfb4574a1a00989e2b771d9 |
| institution | OA Journals |
| issn | 1471-2407 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-50abc76bbbfb4574a1a00989e2b771d92025-08-20T02:22:26ZengBMCBMC Cancer1471-24072024-11-0124111010.1186/s12885-024-13115-7Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stressJin-Biao Xie0Shi-Jie Huang1Tian-Bao Yang2Wu Wang3Bo-Yang Chen4Lianyi Guo5Department of Cardiothoracic Surgery, The Affiliated Hospital of Putian UniversityDepartment of Cardiothoracic Surgery, The Affiliated Hospital of Putian UniversityDepartment of Cardiothoracic Surgery, The Affiliated Hospital of Putian UniversityDepartment of Cardiothoracic Surgery, The Affiliated Hospital of Putian UniversityDepartment of Cardiothoracic Surgery, The Affiliated Hospital of Putian UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Jinzhou Medical UniversitySummary Background Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC). Methods This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots. Results The training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 − 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962–0.987), while the DT model is 0.784 (0.739–0.830) and the SVM is 0.879 (0.843–0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717–0.864), compared to the DT model with an AUC of 0.717 (0.640–0.794) and 0.779 (0.702–0.856) in SVM. Conclusions The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery.https://doi.org/10.1186/s12885-024-13115-7Machine learningEsophageal squamous cancerElderly patientsOxidative stressPrognosis |
| spellingShingle | Jin-Biao Xie Shi-Jie Huang Tian-Bao Yang Wu Wang Bo-Yang Chen Lianyi Guo Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress BMC Cancer Machine learning Esophageal squamous cancer Elderly patients Oxidative stress Prognosis |
| title | Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| title_full | Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| title_fullStr | Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| title_full_unstemmed | Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| title_short | Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| title_sort | comparison of machine learning methods for predicting 3 year survival in elderly esophageal squamous cancer patients based on oxidative stress |
| topic | Machine learning Esophageal squamous cancer Elderly patients Oxidative stress Prognosis |
| url | https://doi.org/10.1186/s12885-024-13115-7 |
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