Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China
Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Patients' baseline characteristics and anthropometric m...
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| Published in | BMC cancer Vol. 24; no. 1; pp. 711 - 13 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
10.06.2024
BioMed Central BMC |
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| Abstract | Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.
Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.
A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.
The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. |
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| AbstractList | Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.BACKGROUNDInflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.METHODSPatients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.RESULTSA 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.CONCLUSIONThe proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. Background Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Methods Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. Results A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. Conclusion The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. Keywords: Machine learning, Gastric cancer, Prognosis, Inflammatory biomarkers, Overall survival Abstract Background Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Methods Patients’ baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. Results A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets – “high-risk” and “low-risk” based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. Conclusion The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. BackgroundInflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.MethodsPatients’ baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.ResultsA 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets – “high-risk” and “low-risk” based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.ConclusionThe proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients. |
| Audience | Academic |
| Author | Zhao, Qingchuan Qiao, Qiuge Li, Suyi Yao, Qinghua Li, Wei Cui, Jiuwei Shi, Hanping Zhang, Shaobo Song, Chunhua He, Ying Feng, Yongdong Guo, Zengqing Chen, Junqiang Xu, Hongxia |
| Author_xml | – sequence: 1 givenname: Shaobo surname: Zhang fullname: Zhang, Shaobo organization: Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China – sequence: 2 givenname: Hongxia surname: Xu fullname: Xu, Hongxia organization: Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China – sequence: 3 givenname: Wei surname: Li fullname: Li, Wei organization: Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China – sequence: 4 givenname: Jiuwei surname: Cui fullname: Cui, Jiuwei organization: Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China – sequence: 5 givenname: Qingchuan surname: Zhao fullname: Zhao, Qingchuan organization: Department of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi, 710032, China – sequence: 6 givenname: Zengqing surname: Guo fullname: Guo, Zengqing organization: Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, 350014, China – sequence: 7 givenname: Junqiang surname: Chen fullname: Chen, Junqiang organization: Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China – sequence: 8 givenname: Qinghua surname: Yao fullname: Yao, Qinghua organization: Department of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital and Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China – sequence: 9 givenname: Suyi surname: Li fullname: Li, Suyi organization: Department of Nutrition and Metabolism of Oncology, Affiliated Provincial Hospital of Anhui Medical University, Hefei, Anhui, 230031, China – sequence: 10 givenname: Ying surname: He fullname: He, Ying organization: Department of Clinical Nutrition, Chongqing General Hospital, Chongqing, 400014, China – sequence: 11 givenname: Qiuge surname: Qiao fullname: Qiao, Qiuge organization: Department of General Surgery, Second Hospital (East Hospital), Hebei Medical University, Shijiazhuang, Hebei, 050000, China – sequence: 12 givenname: Yongdong surname: Feng fullname: Feng, Yongdong organization: Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China – sequence: 13 givenname: Hanping surname: Shi fullname: Shi, Hanping email: shihp@ccmu.edu.cn, shihp@ccmu.edu.cn, shihp@ccmu.edu.cn organization: Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100054, China. shihp@ccmu.edu.cn – sequence: 14 givenname: Chunhua surname: Song fullname: Song, Chunhua email: sch16@zzu.edu.cn, sch16@zzu.edu.cn, sch16@zzu.edu.cn organization: State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, Henan, 450001, China. sch16@zzu.edu.cn |
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| Keywords | Inflammatory biomarkers Prognosis Overall survival Gastric cancer Machine learning |
| Language | English |
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| Snippet | Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a... Background Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to... BackgroundInflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to... Abstract Background Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study... |
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| SubjectTerms | Adult Aged Algorithms Analysis Biochemistry Biological markers Biomarkers, Tumor Blood platelets Blood tests Cancer Cancer patients China - epidemiology Clinical trials Cohort analysis Cohort Studies Committees Electronic health records Evaluation Female Gastric cancer Health aspects Humans Inflammation Inflammatory biomarkers Kaplan-Meier Estimate Learning algorithms Leukocytes Lifestyles Lymphocytes Machine Learning Male Medical prognosis Medical records Middle Aged Mortality Neoplasm Staging Neutrophils Nomograms Oncology, Experimental Overall survival Patients Physiological aspects Prognosis Proportional Hazards Models Quality of life Questionnaires Stomach cancer Stomach Neoplasms - diagnosis Stomach Neoplasms - mortality Stomach Neoplasms - pathology Survival Variables |
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| Title | Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/38858653 https://www.proquest.com/docview/3066881049 https://www.proquest.com/docview/3066791155 https://pubmed.ncbi.nlm.nih.gov/PMC11163779 https://doaj.org/article/613c71b9ae4a44d193d6343c0ea7ec22 |
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