Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Durvalumab plus etoposide-platinum in patients with epidermal growth factor receptor (EGFR)-mutated advanced NSCLC and neuroendocrine transformation after first-line osimertinib: ORCHARD.

Lung cancer (Amsterdam, Netherlands)·2026

Same author

Clinical Characteristics and Survival Outcomes in Patients With Small Cell Lung Cancer in Spain: An Analysis of the Thoracic Tumor Registry (2016-2025).

Archivos de bronconeumologia·2026

Same author

Multidisciplinary Approach to Sexual Dysfunction in Persons With Lung Cancer-Spanish Lung Cancer Group Expert Consensus Statement.

JTO clinical and research reports·2026

Same author

Characteristics and clinical outcomes of a nationwide cohort of patients with small cell lung cancer following first-line treatment: Real-world evidence.

Lung cancer (Amsterdam, Netherlands)·2026

Same author

Plasmatic CXCL13 as a biomarker of primary resistance to immunotherapy and impaired peripheral immunity in non-small cell lung cancer.

Biomarker research·2026

Same author

ARIAN protocol: a phase III study of sacituzumab and zimberelimab for the treatment of patients with non-small cell lung cancer.

Expert opinion on biological therapy·2026

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.

Adrianna Janik¹, Maria Torrente², Luca Costabello¹

¹Accenture Labs, Dublin, Ireland.

JCO Clinical Cancer Informatics

|July 10, 2023

Summary

Machine learning accurately predicts relapse in early-stage non-small-cell lung cancer (NSCLC). This approach personalizes patient care by estimating relapse probability, aiding treatment decisions for lung cancer patients.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Related Experiment Videos

Last Updated: Jul 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Area of Science:

Oncology
Biostatistics
Computer Science

Background:

Personalized cancer care relies on accurate relapse risk stratification.
Early-stage non-small-cell lung cancer (NSCLC) requires precise prognostic tools.

Purpose of the Study:

To investigate the efficacy of machine learning models in estimating relapse probability for early-stage NSCLC patients.
To answer: How to use machine learning to estimate probability of relapse in patients with early-stage non-small-cell lung cancer (NSCLC)?

Main Methods:

Trained tabular and graph machine learning models on data from 1,387 early-stage NSCLC patients.
Employed SHapley Additive exPlanations for tabular model interpretability.
Utilized an example-based method to explain graph model predictions.

Main Results:

Random forest model achieved 76% accuracy in predicting relapse using tabular data (10-fold cross-validation).
Graph machine learning model reached 68% accuracy on a held-out test set.
SHAP values and example-based explanations provided insights into model predictions.

Conclusions:

Machine learning models offer objective, personalized, and reproducible relapse prediction for early-stage NSCLC.
The developed prognostic model shows potential as a decision support tool for adjuvant therapy selection.
Further validation with diverse data is recommended for clinical implementation.