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

453
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...
453
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Epidemiological characteristics of spinal cord injury in Northwest China: a single hospital-based study.

Journal of orthopaedic surgery and research·2020
Same author

An Eu(III)-functionalized Sr-based metal-organic framework for fluorometric determination of Cr(III) and Cr(VI) ions.

Mikrochimica acta·2020
Same author

Switchable solvent N, N, N', N'-tetraethyl-1, 3-propanediamine was dissociated into cationic surfactant to promote cell disruption and lipid extraction from wet microalgae for biodiesel production.

Bioresource technology·2020
Same author

Bakuchiol Attenuates Oxidative Stress and Neuron Damage by Regulating Trx1/TXNIP and the Phosphorylation of AMPK After Subarachnoid Hemorrhage in Mice.

Frontiers in pharmacology·2020
Same author

Alterations of local functional connectivity in lifespan: A resting-state fMRI study.

Brain and behavior·2020
Same author

EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees.

Frontiers in neuroscience·2020

Related Experiment Video

Updated: Sep 10, 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

8.3K

Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.

Tenzin Kunkyab1, Yang Lei1, Hao Guo1

  • 1Department of Radiation Oncology, The Mount Sinai Hospital, New York, New York, USA.

Medical Physics
|August 24, 2025
PubMed
Summary

This study introduces a novel knowledge-based planning (KBP) trade-off model for non-small cell lung cancer (NSCLC) treatment. The model accurately predicts planning variations, improving efficiency and aiding clinical decisions.

Keywords:
knowledge based planningnon‐small cell lung cancertrade‐off prediction

More Related Videos

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

193
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

376

Related Experiment Videos

Last Updated: Sep 10, 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

8.3K
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

193
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

376

Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Knowledge-based planning (KBP) leverages past treatment data to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs).
  • Current KBP methods predict a single planning outcome, neglecting potential trade-offs crucial for clinical decision-making.
  • This limits the optimization of treatment plans for complex cases like non-small cell lung cancer (NSCLC).

Purpose of the Study:

  • To develop a KBP trade-off prediction model for locally advanced NSCLC.
  • To assist clinicians in making informed decisions during the treatment planning process.
  • To explore planning trade-offs beyond a single Pareto optimal point.

Main Methods:

  • Generated 13 VMAT plan variations per patient (n=53), including balanced and trade-off plans with prioritized OAR sparing.
  • Utilized the first three principal components of OAR DVHs as targets and 53 anatomical features as predictors.
  • Trained a random forest multi-output regression model to predict DVH principal components for 13 plan-OAR variations.

Main Results:

  • The KBP trade-off model significantly outperformed a balanced model, showing lower average RMSE (5.32 vs. 27.3).
  • The trade-off model achieved lower mean absolute errors for critical DVH metrics, including spinal cord Dmax, esophagus Dmax, and lung/heart V20Gy/V30Gy.
  • Statistical significance (p < 0.01) was observed for all compared metrics, highlighting the model's superior predictive accuracy.

Conclusions:

  • The developed KBP trade-off model reliably predicts planning variations for NSCLC treatment.
  • This model can serve as a decision support tool, offering feasible trade-off estimations during pre-planning.
  • Integration into workflows can enhance treatment planning efficiency and potentially improve plan quality.