Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 28, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Machine Learning-Based Survival Prediction in Early-Stage Non-Small Cell Lung Cancer: Development and Cross-National

Nikhil Joshi1, Hari Ponnamma Rani1, Maxim Shevtsov2

  • 1Department of Mathematics, National Institute of Technology Warangal, Hanamkonda 506004, Telangana, India.

Journal of Clinical Medicine
|May 27, 2026
PubMed
Summary

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Tumor spheroids: opportunities for <i>in vitro</i> validation of anticancer drugs in imaging and targeted drug delivery.

Therapeutic delivery·2026
Same author

Enhancing the Antibacterial and Biointegrative Properties of Microporous Titanium Surfaces Using Various Metal Coatings: A Comparative Study.

Prosthesis (Basel, Switzerland)·2026
Same author

CLO26-123: Genomic Adjusted Radiation Dose (GARD) Predicts Optimal Radiation Dose for Overall Survival in Triple-Negative Breast Cancer.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Variable loop circular catheter pulsed field ablation in real-world practice: Low complication rates across patient and procedural characteristics in the REAL AF registry.

Heart rhythm·2026
Same author

Complementary constraints in germ and immune cells shape evolution of gene regulation and phenotype.

bioRxiv : the preprint server for biology·2026
Same author

CLO26-123: Genomic Adjusted Radiation Dose (GARD) Predicts Optimal Radiation Dose for Overall Survival in Triple-Negative Breast Cancer.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
This summary is machine-generated.

Machine learning models can predict lung cancer survival across different countries. The Random Survival Forest model showed stability, though long-term accuracy may vary with patient differences.

Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Prognostic models may lack accuracy across diverse populations due to demographic and clinical variations.
  • Investigating cross-national applicability of machine learning (ML) survival models is crucial.

Purpose of the Study:

  • To assess the cross-national applicability of ML-based survival prediction models for lung cancer.
  • To validate models trained on US data using an independent Chinese cohort.
  • To compare the performance of different ML models in predicting survival across populations.

Main Methods:

  • Developed Cox proportional hazards, Random Survival Forest (RSF), and XGBoost-Cox models.
Keywords:
SEER databaseexternal validationlung cancermachine learningnon-small cell lung cancerrandom survival forestsurvival prediction

Related Experiment Videos

Last Updated: May 28, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

  • Externally validated models using US SEER database and a Chinese clinical cohort.
  • Evaluated model discrimination (C-index, AUC) and calibration, using cross-validation for tuning.

    • Main Results:

    • Models were trained on 13,260 US patients and validated on 505 Chinese patients.
    • The Chinese cohort had younger patients and more Stage IA disease.
    • RSF demonstrated the most stability across cohorts (C-index 0.740 US, 0.782 China) and timeframes, with good calibration at 1-3 years.

    Conclusions:

    • ML models, particularly RSF, show promise for predicting cross-population survival in early-stage non-small cell lung cancer (NSCLC).
    • Differences in patient characteristics and treatment can impact long-term model performance.
    • Rigorous external validation is essential for the reliable application of ML models in diverse oncology settings.