Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Journal: Annals of surgical oncology

PMID: 39873848

Abstract

BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).

Authors

Fangchao Ding

Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Yizhen Zhuang

Department of Medical Record Office, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shengxiang Chen

Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. wmu3818csx@163.com.

Keywords

Aged Carcinoma, Neuroendocrine Female Humans Machine Learning Male Middle Aged Nomograms Predictive Learning Models Proportional Hazards Models Random Forest Regression Analysis Retrospective Studies Risk Assessment SEER Program Stomach Neoplasms Survival Rate United States

External Resources

View on PubMed Access via DOI PubMed (39873848)

Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Abstract

Authors

Keywords

External Resources

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