Prognostic Value of a Serological-Based Clinical Model for Gastric Cancer Patients

Area of Science:

• Oncology
• Clinical Diagnostics
• Biostatistics

Background:

• Surgery is the primary treatment for gastric cancer.
• Accurate prognostic evaluation is crucial for patient management.
• Current prognostic tools may require enhancement for better prediction.

Purpose of the Study:

• To develop and validate a serology-based clinical scoring system for gastric cancer prognosis.
• To create a predictive model using preoperative serological indicators.
• To assess the efficacy of the developed score in predicting patient survival.

Main Methods:

• Clinicopathological data from 5493 gastric cancer patients (2009-2018) were analyzed.
• A serum risk score (SerScore) was constructed using LASSO-Cox analysis on 12 selected serological indicators.
• Prognostic prediction models, including a multivariate nomogram, were developed and validated in training and validation cohorts.

Main Results:

• The SerScore effectively differentiated survival rates, with lower scores indicating better outcomes.
• SerScore, age, tumor location, T stage, and N stage were independent prognostic factors.
• The multivariate nomogram achieved high predictive accuracy (C-index 0.745-0.750) and AUC values (0.783-0.805), outperforming SerScore alone.

Conclusions:

• The developed SerScore model is an effective tool for prognostic assessment in gastric cancer patients.
• This serology-based system enhances prognostic evaluation and survival prediction.
• The model serves as a foundation for future advanced gastric cancer prediction tools.