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

Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2026.3.2.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Same author

WABIP/AABIP statement on therapeutic indications of medical thoracoscopy for benign pleural disease.

European respiratory review : an official journal of the European Respiratory Society·2026

Same author

Effectiveness of virtual delivery of cancer education for patients: a systematic review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026

Same author

Lymph Node Sampling Patterns and Completeness of Staging During Systematic Mediastinal Lymph Node Staging in Patients with Locally Advanced Non-Small-Cell Lung Cancer: A Post Hoc Analysis from the SEISMIC Study.

Cancers·2026

Same author

Advances in Diagnosis-Pulmonology.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026

Same author

Resistance to Immune Checkpoint Inhibitor Treatment in Non-Small Cell Lung Cancer Clinical Trials: A Perspective From Lung-MAP Investigators.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Same journal

Effect of a Multidimensional Digital Health Intervention on Quality of Life in Breast Cancer Survivors: A Randomized Controlled Trial.

JCO clinical cancer informatics·2026

Same journal

Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?

JCO clinical cancer informatics·2026

Same journal

Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers.

JCO clinical cancer informatics·2026

Same journal

Foundation Model-Driven Regions of Interest Classification and Renaming in Cancer Radiotherapy: A Customizable, Retraining-Free Workflow Across Institutions.

JCO clinical cancer informatics·2026

Same journal

Announcing a New Article Type in <i>JCO Clinical Cancer Informatics</i>: The Resource Report.

JCO clinical cancer informatics·2026

Same journal

A Harmonized International Database of More Than 10,000 Pediatric Renal Tumor Patients From 30 Years of SIOP-RTSG Studies.

JCO clinical cancer informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 12, 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

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed

Felipe Soares Torres¹, Shazia Akbar², Srinivas Raman³

¹Joint Department of Medical Imaging, Toronto General Hospital, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.

JCO Clinical Cancer Informatics

|November 19, 2021

Summary

This summary is machine-generated.

Deep learning analysis of pretreatment CT scans can predict lung cancer patient mortality, offering prognostic information comparable to TNM staging. Combining imaging data with TNM staging improves risk stratification for better patient management.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Related Experiment Videos

Last Updated: Oct 12, 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

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Area of Science:

Oncology
Radiology
Artificial Intelligence

Background:

Clinical TNM staging is crucial for lung cancer prognosis and treatment planning.
Computed tomography (CT) is central to TNM staging.
Deep learning on pretreatment CTs may provide enhanced prognostic insights.

Purpose of the Study:

To develop and evaluate a deep learning technique (IPRO) for predicting lung cancer patient mortality.
To compare the prognostic performance of IPRO with traditional TNM staging.
To assess the combined efficacy of IPRO and TNM staging for risk stratification.

Main Methods:

Developed a deep learning model (IPRO) to predict 1-, 2-, and 5-year mortality from pretreatment CT scans.
Utilized six public datasets comprising 1,689 lung cancer patients.
Performed cross-validation, comparing IPRO, TNM staging, and an Ensemble risk score.

Main Results:

IPRO demonstrated prognostic power similar to TNM staging for mortality prediction (1-year C-index: 0.72 vs 0.71).
An Ensemble risk score combining IPRO and TNM staging showed superior performance (1-year C-index: 0.77).
IPRO effectively stratified patients within TNM stages, identifying significant survival differences across risk quintiles.

Conclusions:

Deep learning on pretreatment CT scans, when combined with TNM staging, improves prognostication and risk stratification in lung cancer.
This approach offers potential for more precise, individualized mortality risk prediction.