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

Adaptive RAG-Assisted MRI Platform (ARAMP) for Brain Metastasis Detection and Reporting: A Retrospective Evaluation Using Post-Contrast T1-Weighted Imaging.

Bioengineering (Basel, Switzerland)·2025

Same author

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

The international journal of cardiovascular imaging·2025

Same author

Early prediction of radiotherapy outcomes in pharyngeal cancer using deep learning on baseline [18F]Fluorodeoxyglucose positron emission Tomography/Computed tomography.

European journal of radiology·2024

Same author

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

Nuclear medicine communications·2024

Same author

Imaging prediction of <i>KRAS</i> mutation in patients with rectal cancer through deep metric learning using pretreatment [<sup>18</sup>F]Fluorodeoxyglucose positron emission tomography/computed tomography.

The British journal of radiology·2023

Same author

Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease.

Diagnostics (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

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

Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed

Kuo-Chen Wu^1,2, Shang-Wen Chen^3,4,5, Yuan-Yen Chang⁶

¹Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.

|November 13, 2025

Summary

This summary is machine-generated.

This study developed a deep learning model using CT scans to predict local recurrence, neck relapse, and distant metastases in pharyngeal cancer patients undergoing adaptive radiation therapy (ART). The model shows promise in forecasting treatment outcomes for improved patient care.

Keywords:

adaptive radiotherapy computed tomography deep contrastive learning pharyngeal cancer treatment outcome

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Related Experiment Videos

Last Updated: Jan 11, 2026

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

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Area of Science:

Oncology
Radiotherapy
Medical Imaging
Artificial Intelligence

Background:

Adaptive radiation therapy (ART) is increasingly adopted in clinical radiotherapy (RT).
Pharyngeal cancers require precise RT planning and outcome prediction.
Deep learning (DL) offers potential for enhanced treatment outcome prediction in RT.

Purpose of the Study:

To develop a deep learning (DL) model for predicting treatment outcomes in pharyngeal cancer patients.
To integrate baseline and adaptive radiation therapy (ART) simulation CT images for model training.
To predict local recurrence (LR), neck lymph node relapse (NR), and distant metastases (DM).

Main Methods:

Analysis of clinical and imaging data from 162 pharyngeal cancer patients.
Utilized baseline and ART non-contrast simulation CT images for model training.
Employed a deep contrastive learning model with CT image augmentation to predict LR, NR, and DM.

Main Results:

Over a median follow-up of 34 months, LR, NR, and DM occurred in 32.7%, 22.2%, and 14.0% of patients, respectively.
The integrated DL model achieved an Area Under the Curve (AUC) of 0.773 for LR, 0.747 for NR, and 0.793 for DM.
Prediction accuracies for LR, NR, and DM were 72.4%, 74.7%, and 75.7%, respectively.

Conclusions:

The developed DL models can predict LR, NR, and DM in pharyngeal cancer patients using baseline and ART simulation CTs.
The model shows potential for improving treatment outcome prediction in radiotherapy.
External validation is recommended to confirm the model's generalizability and performance.