Claire Chenwen Zhong, Junjie Huang, Zehuan Yang, Zhaojun Li, Yu Jiang, Jinqiu Yuan, Xiaodan Huang, Xiaofang Liu, Queran Lin, Han Wang, Jonathan Poon, Qi Dou, Irene Xin Yin Wu, Martin C. S. Wong
{"title":"机器学习-子宫癌合并2型糖尿病患者生存预测模型：一项区域性队列研究。","authors":"Claire Chenwen Zhong, Junjie Huang, Zehuan Yang, Zhaojun Li, Yu Jiang, Jinqiu Yuan, Xiaodan Huang, Xiaofang Liu, Queran Lin, Han Wang, Jonathan Poon, Qi Dou, Irene Xin Yin Wu, Martin C. S. Wong","doi":"10.1111/jog.70087","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>This study aimed to develop predictive models and establish a risk scoring system to identify risk factors associated with survival in uterine cancer patients with type 2 diabetes (T2D) and estimate their survival probabilities.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Data were collected from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL) from 2000 to 2020. Cox proportional hazards regression, survival tree, LASSO Cox regression, boosting, and random survival forest (RSF) were utilized to develop predictive models for survival. Key risk factors were identified through Shapley Additive Explanations analysis, whereas the AutoScore-Survival package facilitated the development of a risk scoring system.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>This cohort study included 2047 uterine cancer patients with T2D. The average survival time was 100.82 (standard deviation: 72.75) months. The RSF model demonstrated the strongest predictive performance, achieving a time-dependent area under the curve (AUC) of 0.823 and a <i>C</i>-index of 0.90. A risk scoring system was created based on several criteria: age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium level, low-density lipoprotein cholesterol level (LDL-C) level, body mass index (BMI), and triglycerides level. This scoring system classified 31.4% of patients as high-risk, resulting in a 5-year survival probability of 43.5%, about 1.7 times lower than that of the low-risk group.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This study leveraged machine learning to identify key survival predictors and develop a clinically interpretable risk scoring system for uterine cancer patients with T2D. Key predictors, including age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium levels, LDL-C levels, BMI, and triglycerides levels, effectively stratified survival risk. These findings demonstrate the potential of data-driven models to enhance individualized prediction and inform targeted clinical management.</p>\n </section>\n </div>","PeriodicalId":16593,"journal":{"name":"Journal of Obstetrics and Gynaecology Research","volume":"51 10","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12484716/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning–Predictive Models for Survival in Uterine Cancer Patients With Type 2 Diabetes: A Territory-Wide Cohort Study\",\"authors\":\"Claire Chenwen Zhong, Junjie Huang, Zehuan Yang, Zhaojun Li, Yu Jiang, Jinqiu Yuan, Xiaodan Huang, Xiaofang Liu, Queran Lin, Han Wang, Jonathan Poon, Qi Dou, Irene Xin Yin Wu, Martin C. S. Wong\",\"doi\":\"10.1111/jog.70087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>This study aimed to develop predictive models and establish a risk scoring system to identify risk factors associated with survival in uterine cancer patients with type 2 diabetes (T2D) and estimate their survival probabilities.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Data were collected from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL) from 2000 to 2020. Cox proportional hazards regression, survival tree, LASSO Cox regression, boosting, and random survival forest (RSF) were utilized to develop predictive models for survival. Key risk factors were identified through Shapley Additive Explanations analysis, whereas the AutoScore-Survival package facilitated the development of a risk scoring system.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>This cohort study included 2047 uterine cancer patients with T2D. The average survival time was 100.82 (standard deviation: 72.75) months. The RSF model demonstrated the strongest predictive performance, achieving a time-dependent area under the curve (AUC) of 0.823 and a <i>C</i>-index of 0.90. A risk scoring system was created based on several criteria: age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium level, low-density lipoprotein cholesterol level (LDL-C) level, body mass index (BMI), and triglycerides level. This scoring system classified 31.4% of patients as high-risk, resulting in a 5-year survival probability of 43.5%, about 1.7 times lower than that of the low-risk group.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>This study leveraged machine learning to identify key survival predictors and develop a clinically interpretable risk scoring system for uterine cancer patients with T2D. Key predictors, including age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium levels, LDL-C levels, BMI, and triglycerides levels, effectively stratified survival risk. 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Machine Learning–Predictive Models for Survival in Uterine Cancer Patients With Type 2 Diabetes: A Territory-Wide Cohort Study
Aim
This study aimed to develop predictive models and establish a risk scoring system to identify risk factors associated with survival in uterine cancer patients with type 2 diabetes (T2D) and estimate their survival probabilities.
Methods
Data were collected from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL) from 2000 to 2020. Cox proportional hazards regression, survival tree, LASSO Cox regression, boosting, and random survival forest (RSF) were utilized to develop predictive models for survival. Key risk factors were identified through Shapley Additive Explanations analysis, whereas the AutoScore-Survival package facilitated the development of a risk scoring system.
Results
This cohort study included 2047 uterine cancer patients with T2D. The average survival time was 100.82 (standard deviation: 72.75) months. The RSF model demonstrated the strongest predictive performance, achieving a time-dependent area under the curve (AUC) of 0.823 and a C-index of 0.90. A risk scoring system was created based on several criteria: age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium level, low-density lipoprotein cholesterol level (LDL-C) level, body mass index (BMI), and triglycerides level. This scoring system classified 31.4% of patients as high-risk, resulting in a 5-year survival probability of 43.5%, about 1.7 times lower than that of the low-risk group.
Conclusion
This study leveraged machine learning to identify key survival predictors and develop a clinically interpretable risk scoring system for uterine cancer patients with T2D. Key predictors, including age at cancer diagnosis, duration of T2D, creatinine levels, serum potassium levels, LDL-C levels, BMI, and triglycerides levels, effectively stratified survival risk. These findings demonstrate the potential of data-driven models to enhance individualized prediction and inform targeted clinical management.
期刊介绍：
The Journal of Obstetrics and Gynaecology Research is the official Journal of the Asia and Oceania Federation of Obstetrics and Gynecology and of the Japan Society of Obstetrics and Gynecology, and aims to provide a medium for the publication of articles in the fields of obstetrics and gynecology.
The Journal publishes original research articles, case reports, review articles and letters to the editor. The Journal will give publication priority to original research articles over case reports. Accepted papers become the exclusive licence of the Journal. Manuscripts are peer reviewed by at least two referees and/or Associate Editors expert in the field of the submitted paper.
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