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

Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

505
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
505

You might also read

Related Articles

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

Sort by
Same author

A novel NAMPT activator ameliorates obesity by repressing ACSL1-dependent lipid synthesis.

Acta pharmaceutica Sinica. B·2026
Same author

A multicenter retrospective analysis of canine idiopathic epilepsy in China.

Frontiers in veterinary science·2026
Same author

Functionalized Fluorescent Nanodiamonds Reveal Therapeutic Protein Clearance Through ENDOTAC Linked to AUTOTAC.

Advanced healthcare materials·2026
Same author

Efficacy of NEPA for prevention of chemotherapy induced nausea and vomiting in head and neck cancer patients receiving cisplatin-based chemotherapy.

European journal of clinical pharmacology·2026
Same author

Subject: Author response to "Comment on 'Identification of the molecular characterization and tumor microenvironment of thoracic inflammatory myofibroblastic tumors"' (JFMA-D-26-00819).

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same author

Response to letter to the editor "Revisiting outcome interpretation and tumor microenvironment characterization in thoracic inflammatory myofibroblastic tumors".

Journal of the Formosan Medical Association = Taiwan yi zhi·2026

Related Experiment Video

Updated: Jan 10, 2026

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

7.3K

Using Radiomics and Explainable Ensemble Learning to Predict Radiation Pneumonitis and Survival in NSCLC Patients

Tsair-Fwu Lee1,2,3, Lawrence Tsai1, Po-Shun Tseng1

  • 1Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung 80778, Taiwan.

Life (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

This study developed a precise model using radiomics and AI to predict radiation pneumonitis and survival in non-small cell lung cancer patients after VMAT. The model achieved high accuracy, enhancing personalized radiotherapy.

Keywords:
ensemble learningexplainable artificial intelligencelung cancermachine learningradiation pneumonitisradiomicssurvival analysisvolumetric modulated arc therapy

More Related Videos

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

8.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Related Experiment Videos

Last Updated: Jan 10, 2026

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

7.3K
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

8.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Area of Science:

  • Oncology
  • Medical Physics
  • Artificial Intelligence

Background:

  • Non-small cell lung cancer (NSCLC) patients undergoing VMAT are at risk of radiation pneumonitis (RP) and have varying survival rates.
  • Precise prediction of RP and survival is crucial for optimizing radiotherapy and improving patient outcomes.
  • Current predictive models may lack the granularity to capture complex individual patient responses to VMAT.

Purpose of the Study:

  • To develop a precise predictive model for radiation pneumonitis (RP) and three-year survival in NSCLC patients treated with VMAT.
  • To integrate radiomics features, ensemble stacking, and explainable AI (XAI) to enhance predictive accuracy and clinical interpretability.
  • To identify key predictive factors for RP and survival using advanced feature selection and interpretation techniques.

Main Methods:

  • Retrospective analysis of 221 NSCLC patients treated with VMAT, including clinical data, DVH parameters, and radiomic features.
  • Feature selection using ANOVA, LASSO, and Boruta-SHAP, followed by ensemble stacking of six machine learning models (LR, RF, SVM, KNN, XGBoost, Ensemble Stacking).
  • Model performance evaluated using AUC, accuracy, NPV, precision, and F1 score, with SHAP analysis for feature interpretation.

Main Results:

  • The LASSO-selected radiomic subset with Ensemble Stacking achieved an AUC of 0.91 for RP prediction and 0.97 for survival prediction.
  • V40 Firstorder_Min was the most influential feature for RP prediction, while V10 Wavelet Firstorder_Min was key for survival prediction.
  • Multimodal subsets also showed strong performance, indicating the value of integrating diverse data types.

Conclusions:

  • Radiomics combined with Ensemble Stacking and XAI significantly improves the prediction of RP and survival in NSCLC patients undergoing VMAT.
  • SHAP-based interpretation enhances model transparency and clinical trust, supporting personalized radiotherapy.
  • The developed models provide a robust foundation for precision medicine in radiation oncology.