Machine Learning–Predictive Models for Survival in Uterine Cancer Patients With Type 2 Diabetes: A Territory‐Wide Cohort Study
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Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Machine Learning–Predictive Models for Survival in Uterine Cancer Patients With Type 2 Diabetes: A Territory‐Wide Cohort Study
- Date Crossref
- 30/09/2025
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese University of Hong Kong Centre for Health Education and Health Promotion pays non établi dans la noticeUniversité ou école supérieure
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Hong Kong Jockey Club pays non établi dans la noticeOrganisation à but non lucratif
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Sun Yat-sen University Clinical Research Center & Big Data Center pays non établi dans la noticeUniversité ou école supérieure
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The Seventh Affiliated Hospital of Sun Yat-sen University pays non établi dans la noticeÉtablissement de santé
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Sun Yat-sen University Cancer Center pays non établi dans la noticeÉtablissement de santé
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State Key Laboratory of Oncology in South China pays non établi dans la noticeStructure de recherche
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Nankai University Institute of Robotics and Automatic Information Systems pays non établi dans la noticeUniversité ou école supérieure
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Public Health England pays non établi dans la noticeOrganisme public
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Sun Yat-sen Memorial Hospital pays non établi dans la noticeÉtablissement de santé
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Faculty of Public Health pays non établi dans la noticeOrganisation à but non lucratif
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Imperial College London pays non établi dans la noticeUniversité ou école supérieure
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Tongji Hospital pays non établi dans la noticeÉtablissement de santé
Centre for Health Education and Health Promotion — Chinese University of Hong Kong, Hong Kong Jockey Club et Clinical Research Center & Big Data Center — Sun Yat-sen University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.