Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers
Abstract Background The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear. Methods This retrospective multicenter study included consecutive patients with GC aged ≥ 6...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-025-13545-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571618255175680 |
|---|---|
| author | Xing-Qi Zhang Ze-Ning Huang Ju Wu Chang-Yue Zheng Xiao-Dong Liu Ying-Qi Huang Qi-Yue Chen Ping Li Jian-Wei Xie Chao-Hui Zheng Jian-Xian Lin Yan-Bing Zhou Chang-Ming Huang |
| author_facet | Xing-Qi Zhang Ze-Ning Huang Ju Wu Chang-Yue Zheng Xiao-Dong Liu Ying-Qi Huang Qi-Yue Chen Ping Li Jian-Wei Xie Chao-Hui Zheng Jian-Xian Lin Yan-Bing Zhou Chang-Ming Huang |
| author_sort | Xing-Qi Zhang |
| collection | DOAJ |
| description | Abstract Background The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear. Methods This retrospective multicenter study included consecutive patients with GC aged ≥ 65 years treated between January 2012 and April 2018. The patients were allocated into three cohorts (training, internal, and external validation). The GC-Integrated Oxidative Stress Score (GIOSS) was developed using Cox regression to correlate biochemical markers with patient prognosis. Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. The SHapley Additive exPlanations (SHAP) method was used for model interpretation. Results This study included a total of 1,859 older patients. The results demonstrated that a low GIOSS was a predictor of poor prognosis. RF was the most efficient method, with AUCs of 0.999, 0.869, and 0.796 in the training, internal validation, and external validation sets, respectively. The DT and SVM models showed low AUC values. Calibration and decision curve analyses demonstrated the considerable clinical usefulness of the RF model. The SHAP results identified pN, pT, perineural invasion, tumor size, and GIOSS as key predictive features. An online web calculator was constructed based on the best model. Conclusions Incorporating the GIOSS, the RF model effectively predicts postoperative OS in older patients with GC and is a robust prognostic tool. Our findings emphasize the importance of oxidative stress in cancer prognosis and provide a pathway for improved management of GC. Trial registration Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024–05-01). |
| format | Article |
| id | doaj-art-da0f3edbc9bc4a9b984d15a2e6b7d3ef |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-da0f3edbc9bc4a9b984d15a2e6b7d3ef2025-02-02T12:28:58ZengBMCBMC Cancer1471-24072025-02-0125111210.1186/s12885-025-13545-xDevelopment and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markersXing-Qi Zhang0Ze-Ning Huang1Ju Wu2Chang-Yue Zheng3Xiao-Dong Liu4Ying-Qi Huang5Qi-Yue Chen6Ping Li7Jian-Wei Xie8Chao-Hui Zheng9Jian-Xian Lin10Yan-Bing Zhou11Chang-Ming Huang12Department of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of General Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of Gastric Surgery, Fujian Medical University Union HospitalDepartment of General Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Gastric Surgery, Fujian Medical University Union HospitalAbstract Background The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear. Methods This retrospective multicenter study included consecutive patients with GC aged ≥ 65 years treated between January 2012 and April 2018. The patients were allocated into three cohorts (training, internal, and external validation). The GC-Integrated Oxidative Stress Score (GIOSS) was developed using Cox regression to correlate biochemical markers with patient prognosis. Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. The SHapley Additive exPlanations (SHAP) method was used for model interpretation. Results This study included a total of 1,859 older patients. The results demonstrated that a low GIOSS was a predictor of poor prognosis. RF was the most efficient method, with AUCs of 0.999, 0.869, and 0.796 in the training, internal validation, and external validation sets, respectively. The DT and SVM models showed low AUC values. Calibration and decision curve analyses demonstrated the considerable clinical usefulness of the RF model. The SHAP results identified pN, pT, perineural invasion, tumor size, and GIOSS as key predictive features. An online web calculator was constructed based on the best model. Conclusions Incorporating the GIOSS, the RF model effectively predicts postoperative OS in older patients with GC and is a robust prognostic tool. Our findings emphasize the importance of oxidative stress in cancer prognosis and provide a pathway for improved management of GC. Trial registration Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024–05-01).https://doi.org/10.1186/s12885-025-13545-xElderlyGastric cancerOxidative stressMachine learningOverall survival |
| spellingShingle | Xing-Qi Zhang Ze-Ning Huang Ju Wu Chang-Yue Zheng Xiao-Dong Liu Ying-Qi Huang Qi-Yue Chen Ping Li Jian-Wei Xie Chao-Hui Zheng Jian-Xian Lin Yan-Bing Zhou Chang-Ming Huang Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers BMC Cancer Elderly Gastric cancer Oxidative stress Machine learning Overall survival |
| title | Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| title_full | Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| title_fullStr | Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| title_full_unstemmed | Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| title_short | Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| title_sort | development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers |
| topic | Elderly Gastric cancer Oxidative stress Machine learning Overall survival |
| url | https://doi.org/10.1186/s12885-025-13545-x |
| work_keys_str_mv | AT xingqizhang developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT zeninghuang developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT juwu developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT changyuezheng developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT xiaodongliu developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT yingqihuang developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT qiyuechen developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT pingli developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT jianweixie developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT chaohuizheng developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT jianxianlin developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT yanbingzhou developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers AT changminghuang developmentandvalidationofaprognosticpredictionmodelforelderlygastriccancerpatientsbasedonoxidativestressbiochemicalmarkers |