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

Cancer Survival Analysis01:21

Cancer Survival Analysis

345
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...
345

You might also read

Related Articles

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

Sort by
Same author

Advancing Predictive Modeling of Inflammatory Bowel Disease (IBD) Flares: A Data-Driven Approach Using Lifestyle and Psychosocial Factors from a Remote Monitoring Platform.

Digestive diseases and sciences·2026
Same author

Challenges and opportunities for real-world evidence in clinical oncology-a view from the UK: proceedings of a national workshop.

ESMO real world data and digital oncology·2026
Same author

Benefits and Limitations of Real-World Patient-Reported Toxicity Symptom Monitoring for Guidelines and Care, as Perceived by Patients, Clinicians, and Guideline Developers.

Cancer medicine·2025
Same author

An Overview of Real-World Data Infrastructure for Cancer Research.

Clinical oncology (Royal College of Radiologists (Great Britain))·2024
Same author

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs).

Clinical and translational radiation oncology·2022
Same author

A Glimmer of Hope Within the Mountain of Hype - Reviewing the Role of Artificial Intelligence in Radiotherapy.

Clinical oncology (Royal College of Radiologists (Great Britain))·2021
Same journal

Broad-Spectrum Antibiotics are Associated with worse Survival After Radical Treatment for Glioblastoma Multiforme: A Multicentre Study.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same journal

Exploring the Need for Plan Adaptation in Robustly Optimised Head and Neck Intensity Modulated Proton Therapy: A Dosimetric Perspective From Initial Institutional Experience.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same journal

Lifting the Lid on Best Practice in a Case of Oligometastatic Breast Cancer.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same journal

Standardisation of Imaging Surveillance for Patients With Metastatic Melanoma Stopping Immune Checkpoint Inhibitors.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same journal

Valuing Public Sector Data Assets: Methodological Perspectives From the Add-Aspirin Trial's Use of Healthcare Systems Data for Clinical Follow-Up.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same journal

Autologous Cell Therapy in Oncology: Current Landscape and Future Directions.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

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

8.2K

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell

M Field1, S Vinod1, G P Delaney1

  • 1South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|April 17, 2024
PubMed
Summary
This summary is machine-generated.

A new model using federated learning predicts survival for non-small cell lung cancer (NSCLC) patients. This tool can help personalize radiotherapy decisions, potentially increasing survival rates.

Keywords:
Decision supportfederated learninglung cancermachine learningradiation oncology

More Related Videos

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.7K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K

Related Experiment Videos

Last Updated: Jun 28, 2025

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

8.2K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.7K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.3K

Area of Science:

  • Oncology
  • Radiotherapy
  • Machine Learning
  • Medical Informatics

Background:

  • Non-small cell lung cancer (NSCLC) treatment decisions involve balancing curative and palliative radiotherapy.
  • Accurate survival prediction is crucial for optimizing treatment strategies in inoperable NSCLC.
  • Federated learning offers a way to build robust models using data from multiple institutions without compromising privacy.

Purpose of the Study:

  • To develop a two-year overall survival model for inoperable stage I-III NSCLC patients.
  • To utilize routine radiation oncology data within a federated learning network.
  • To evaluate the decision support potential for guiding curative versus palliative radiotherapy choices.

Main Methods:

  • A federated infrastructure was established across seven clinics for data extraction, de-identification, and standardization.
  • A logistic regression model was trained on patient data from 2011-2016 and validated on data from 2017-2019.
  • Model performance was assessed using ROC curves, AUC, C-index, calibration metrics, and Kaplan-Meier survival curves.

Main Results:

  • The study included 1655 patient datasets, with an overall model AUC of 0.68.
  • A significant portion of patients receiving palliative radiotherapy were predicted as low-to-moderate risk.
  • Simulated treatment based on model predictions showed an estimated 11% increase in two-year survival rates.

Conclusions:

  • Federated learning enables the development and validation of decision support systems for lung cancer using multi-institutional data.
  • The developed model can quantify the impact of its use in clinical practice.
  • This approach supports personalized medicine by integrating routine patient data for tailored treatment decisions.