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:

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.

Method

Machine learning models and nomograms to forecast PFS in patients undergoing radical gastrectomy for nonmetastatic gastric cancer with dMMR. Independent risk factors were identified using Cox regression analysis to develop the nomogram. The performance of the models was assessed through C-index, time receiver operating characteristic (T-ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram's risk scores.

Results

Among the 582 patients studied, machine learning models exhi

SUBMITTER: Li Y 

PROVIDER: S-EPMC11759429 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature