A novel prognosis prediction model after completion gastrectomy for remnant gastric cancer: Development and...

Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy.

A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20).

Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P = .001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15).

Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.