Exploring the prognosis of rictor(+) in high-risk population: lasso regression for enhancing overall survival in primary gastric adenocarcinoma.

OBJECTIVE

To identify optimal methods for predicting survival in high-risk Rictor (+) gastric adenocarcinoma patients post-gastrectomy.

METHODS

676 patients diagnosed with gastric adenocarcinoma and Rictor (+) status who underwent radical gastrectomy at three medical centers between May 2005 and December 2022 were analyzed. The prognostic variables linked with overall survival were meticulously evaluated through the sophisticated lenses of multiple Cox Proportional Hazards regression and Lasso regression analyses.

RESULTS

The Cox regression model included gender, age, positive lymph node count, and biomarkers (HER2, P27, P53), achieving a C-index of 0.759 (95% CI: 0.723–0.796). The Lasso regression model incorporated age, pT stage, lymph node involvement, neural invasion, and tumor diameter, demonstrating superior predictive accuracy (C-index: 0.797; 95% CI: 0.729–0.865). Both models outperformed the AJCC 8th edition TNM staging system (C-index: 0.69, 95% CI: 0.659–0.722). Calibration curves revealed strong concordance between predicted and observed survival outcomes. Decision curve analysis affirmed clinical utility, while risk reclassification metrics showed significant improvements over AJCC staging: net reclassification improvement (NRI) reached 0.218 (P < 0.01) and 0.178 (P = 0.033) in training and validation cohorts, respectively, with integrated discrimination improvement (IDI) values of 0.085 (P < 0.01) and 0.059 (P = 0.028).

CONCLUSION

Our findings underscore the enhanced predictive accuracy of Lasso regression over the traditional Cox regression model for identifying high-risk gastric adenocarcinoma patients with Rictor (+) status post-gastrectomy. This novel analytical approach may represent a pivotal advance in the stratification of patients at elevated risk, potentially guiding more precise preventive and therapeutic strategies.