JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

  • 0Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China.
World Journal of Gastrointestinal Oncology +

|

February 17, 2025

|

February 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a nomogram to predict gastric cancer survival, identifying ten key prognostic factors. The model offers more accurate outcomes than TNM staging, aiding personalized patient care.

Area Of Science

  • Oncology
  • Medical Statistics
  • Bioinformatics

Background

  • Gastric carcinoma presents high prevalence and mortality in China.
  • Accurate forecasting of overall survival (OS) is crucial for clinical management.

Purpose Of The Study

  • Develop and validate a nomogram for precise gastric cancer prevention and treatment guidance.
  • Improve survival outcome prediction for gastric carcinoma patients.

Main Methods

  • Utilized data from hospitalized gastric cancer patients (2018-2020).
  • Employed Cox regression analyses to identify independent prognostic factors.
  • Developed and evaluated a nomogram model using ROC curves, Kaplan-Meier, and decision curve analyses.

Main Results

  • Identified ten independent prognostic factors: BMI, TNM stage, radiation, chemotherapy, surgery, albumin, globulin, neutrophil count, LDH, and PLR.
  • Achieved strong predictive performance with AUC values for 1-, 3-, and 5-year survival exceeding 0.786 in the validation set.
  • The nomogram demonstrated superior clinical utility compared to TNM staging.

Conclusions

  • A novel nomogram for predicting gastric cancer OS was successfully established and validated.
  • The model exhibits strong predictive ability, supporting personalized interventions.
  • This tool can assist clinicians in optimizing gastric cancer patient management.

Keywords:

Related Concept Videos

Cancer Survival Analysis 01:21

321

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Comparing the Survival Analysis of Two or More Groups 01:20

122

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Kaplan-Meier Approach 01:24

79

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...