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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Leonurine alleviates lung ischemia-reperfusion injury through suppression of ferroptosis via RORα in male mice.

The Journal of endocrinology·2026
Same author

Treatment of Systemic Sclerosis-Associated Interstitial Lung Disease: A Systematic Review and Network Meta-Analysis.

Archives of rheumatology·2025
Same author

Machine learning predicts severe adverse events and salvage success of CT-guided lung biopsy after nondiagnostic transbronchial lung biopsy.

European radiology·2025
Same author

Correlation between the neutrophil-to-lymphocyte ratio and the 90-day all-cause mortality in patients with acute respiratory failure: a retrospective analysis based on the MIMIC-IV Database.

BMC cardiovascular disorders·2025
Same author

Akkermansia muciniphila outer membrane protein regulates recruitment of CD8<sup>+</sup> T cells in lung adenocarcinoma and through JAK-STAT signalling pathway.

Microbial biotechnology·2024
Same author

Efficacy and safety of casirivimab and imdevimab for preventing and treating COVID-19: a systematic review and meta-analysis.

Journal of thoracic disease·2024
Same journal

Correction: Call for Decision Support for Electrocardiographic Alarm Administration Among Neonatal Intensive Care Unit Staff: Multicenter, Cross-Sectional Survey.

Journal of medical Internet research·2026
Same journal

A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges.

Journal of medical Internet research·2026
Same journal

Using a Large Language Model to Support Thematic Analysis of Patient Experiences in Chronic Illness Management: Comparative Qualitative Study.

Journal of medical Internet research·2026
Same journal

Combined Internet-Based Cognitive Behavioral Therapy and Face-to-Face Physiotherapy in Primary Health Care for Chronic Widespread Pain: Randomized Controlled Trial.

Journal of medical Internet research·2026
Same journal

Operationalizing Digital Health Equity in Artificial Intelligence-Enabled Patient Decision Aids for Older Adults: Mixed Methods Study.

Journal of medical Internet research·2026
Same journal

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

Journal of medical Internet research·2026
See all related articles

Related Experiment Video

Updated: Apr 7, 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

863

Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer:

Junpei Wu1,2, Ye Zhang3, Jiaye Wang4

  • 1Department of Pulmonary and Critical Care Medicine, Yiwu Central Hospital, Yiwu, China.

Journal of Medical Internet Research
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

Radiomics-based machine learning (ML) models effectively predict non-small cell lung cancer (NSCLC) recurrence risk, showing high accuracy in both training and validation sets. Further standardization of radiomics workflows is recommended for improved generalizability.

Keywords:
MLNSCLCmachine learningnon–small cell lung cancerprognostic predictionradiomicsrecurrence

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.9K

Related Experiment Videos

Last Updated: Apr 7, 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

863
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.9K

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Early detection of non-small cell lung cancer (NSCLC) recurrence risk remains a clinical challenge.
  • Radiomics-based machine learning (ML) models show promise for predicting NSCLC recurrence.
  • Systematic evidence on the efficiency of these models is currently insufficient.

Conclusions:

  • Radiomics-based ML models are confirmed as effective tools for predicting NSCLC recurrence risk.
  • The findings support the development and updating of these predictive models.
  • Concerns regarding current radiomics methodology necessitate standardization of workflows and use of multicenter data for enhanced generalizability.