Published online Apr 7, 2025. doi: 10.3748/wjg.v31.i13.104466
Revised: February 26, 2025
Accepted: March 19, 2025
Published online: April 7, 2025
Processing time: 102 Days and 20.2 Hours
The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.
To develop positive lymph node ratio (LNR) and machine learning (ML)-based staging systems for GC patients with varying differentiation.
This multicenter retrospective cohort study included 11772 GC patients, with 5612 in the training set (Harbin Medical University Cancer Hospital) and 6160 in the validation set (Surveillance, Epidemiology, and End Results Program database). X-tile software identified optimal cutoff values for the positive LNR, and five ML models were developed using pT and LNR staging. Risk scores were divided into seven stages, constructing new staging systems tailored to different tumor differentiation levels.
In both the training and validation sets, regardless of the tumor differentiation level, LNR staging demonstrated superior prognostic stratification compared to pN. Extreme Gradient Boosting exhibited better predictive performance than the other four models. Compared to tumor node metastasis staging, the new staging systems, developed for patients with different degrees of differentiation, showed significantly better predictive performance.
The new positive lymph nodes ratio staging and integrated staging systems constructed for GC patients with different differentiation grades exhibited better prognostic stratification capabilities.
Core Tip: This study introduces new machine learning-based gastric cancer (GC) staging systems, which incorporate the positive lymph node ratio and pT stages, tailored for well/moderately differentiated GC and poorly differentiated GC patients, respectively. These novel systems demonstrate superior prognostic accuracy compared to the traditional American Joint Committee on Cancer tumor node metastasis staging system, providing a more precise tool for predicting overall survival in resectable GC patients and guiding personalized treatment strategies.