Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR).

Journal: BMC cancer

PMID: 39856598

Abstract

OBJECTIVE: To assess the effectiveness of a machine learning framework and nomogram in predicting progression-free survival (PFS) post-radical gastrectomy in patients with dMMR.

Authors

Yifan Li

College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA.
Jinfeng Ma

Department of General Surgery, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Wenhua Cheng

Department of Gastroenterology, Shanxi Hospital Affiliated to Carcinoma Hospital, Chinese Academy of Medical SciencesShanxi Province Carcinoma Hospital, Carcinoma Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, 030013, People's Republic of China. a15035126358@163.com.

Keywords

Adult Aged Female Gastrectomy Humans Machine Learning Male Middle Aged Nomograms Prognosis Progression-Free Survival Risk Factors ROC Curve Stomach Neoplasms

External Resources

View on PubMed Access via DOI PubMed (39856598)

Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR).

Abstract

Authors

Keywords

External Resources

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