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

You might also read

Related Articles

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

Sort by
Same author

Safety and Long-Term Effect Assessment of Neoadjuvant Chemoradiotherapy for Elderly Patients With Locally Advanced Rectal Cancer: A CHN Single-Center Retrospective Study.

Technology in cancer research & treatment·2020
Same author

BMI May Be a Prognostic Factor for Local Advanced Rectal Cancer Patients Treated with Long-Term Neoadjuvant Chemoradiotherapy.

Cancer management and research·2020
Same journal

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026
Same journal

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026
Same journal

Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.

JMIR medical informatics·2026
Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026
Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026
Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

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

8.9K

Rectal Cancer Radiotherapy Response Prediction: Retrospective Study of Development of a Deep Learning-Based Radiomics

Yiqun Li1, Hengchang Liu2, Qiang Wei1

  • 1Department of Colorectal and Anal Surgery, Shanxi Provincial People's Hospital, No. 29 Shuangtasi Street, Taiyuan, 030012, China, 86 0351-4960080.

JMIR Medical Informatics
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning radiomics using Transformer models accurately predict radiotherapy response in rectal cancer patients. This MRI-based approach offers improved clinical decision-making for personalized treatment strategies.

Keywords:
Transformer modeldeep learningradiomicsradiotherapyrectal cancer

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

844
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

7.6K

Related Experiment Videos

Last Updated: Mar 19, 2026

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

8.9K
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

844
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

7.6K

Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Radiotherapy (RT) is crucial for rectal cancer (RC) treatment, but patient response varies significantly.
  • Predicting RT response before treatment is key for personalized medicine.
  • Deep learning (DL) radiomics shows promise for predicting treatment outcomes.

Purpose of the Study:

  • To develop and compare DL radiomics models for predicting RT response in RC.
  • To evaluate the performance and clinical utility of Transformer architectures for this task.
  • To assess the impact of combining CT and MRI data.

Main Methods:

  • Retrospective analysis of 2000 RC patients treated with RT.
  • Utilized pretreatment CT and MRI scans, along with clinical data.
  • Developed and compared Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), and Transformer DL models.
  • Assessed model performance using AUROC and accuracy, with clinical utility evaluated by decision curve analysis.

Main Results:

  • The Transformer model achieved the highest accuracy (87.0%) and AUROC (0.921) on the test set using MRI-only data.
  • Transformer significantly outperformed CNN and GCN models (P=.02 and P=.041, respectively).
  • Late fusion of CT and MRI data showed comparable AUROC to MRI alone (0.926 vs 0.921; P=.36).

Conclusions:

  • An MRI-driven Transformer DL radiomics model effectively predicts RT response in RC.
  • The Transformer model demonstrated superior performance and clinical utility compared to other DL architectures.
  • Combining CT and MRI data offered limited additional benefit in overall discrimination.