Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study

Nomogram model including LATS2 expression was constructed to predict the prognosis of advanced gastric cancer after surgery

BACKGROUND

Gastric cancer is a leading cause of cancer-related deaths worldwide. Prognostic assessments are typically based on the tumor-node-metastasis (TNM) staging system, which does not account for the molecular heterogeneity of this disease. LATS2, a tumor suppressor gene involved in the Hippo signaling pathway, has been identified as a potential prognostic biomarker in gastric cancer.

AIM

To construct and validate a nomogram model that includes LATS2 expression to predict the survival prognosis of advanced gastric cancer patients following radical surgery, and compare its predictive performance with traditional TNM staging.

METHODS

A retrospective analysis of 245 advanced gastric cancer patients from the Fourth Hospital of Hebei Medical University was conducted. The patients were divided into a training group (171 patients) and a validation group (74 patients) to develop and test our prognostic model. The performance of the model was determined using C-indices, receiver operating characteristic curves, calibration plots, and decision curves.

RESULTS

The model demonstrated a high predictive accuracy with C-indices of 0.829 in the training set and 0.862 in the validation set. Area under the curve values for three-year and five-year survival prediction were significantly robust, suggesting an excellent discrimination ability. Calibration plots confirmed the high concordance between the predictions and actual survival outcomes.

CONCLUSION

We developed a nomogram model incorporating LATS2 expression, which significantly outperformed conventional TNM staging in predicting the prognosis of advanced gastric cancer patients postsurgery. This model may serve as a valuable tool for individualized patient management, allowing for more accurate stratification and improved clinical outcomes. Further validation in larger patient cohorts will be necessary to establish its generalizability and clinical utility.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.