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

Integrated CT-based assessment of muscle and adiposity for risk stratification in advanced pancreatobiliary cancer.

Frontiers in nutrition·2026

Same author

A proof-of-concept automated method for accurate skin dosimetry: correcting overestimated surface dose measurements.

Physics in medicine and biology·2026

Same author

Susceptibility of Large Language Models to User-Driven Factors in Medical Queries.

Journal of healthcare informatics research·2026

Same author

Comparative analysis of radiation therapy plans before and after biodegradable hydrogel (SpaceOAR) injection for reducing rectal toxicity in patients with prostate cancer undergoing carbon ion radiotherapy.

Frontiers in oncology·2026

Same author

Deep learning-based auto-segmentation and RECIST evaluation after concurrent chemoradiotherapy in locally advanced hepatocellular carcinoma patients.

Frontiers in oncology·2026

Same author

Response to "Optimizing palliative radiotherapy for breast cancer with skin involvement: technical and methodologic considerations".

Radiation oncology journal·2026

Search research articles

Related Experiment Video

Updated: Jun 9, 2025

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

LLM-driven multimodal target volume contouring in radiation oncology.

Yujin Oh¹, Sangjoon Park^2,3, Hwa Kyung Byun⁴

¹Department of Radiology, Massachusetts General Hospital (MGH) and Harvard Medical School, Boston, MA, USA.

Nature Communications

|October 25, 2024

Summary

A new AI model, LLMSeg, uses large language models (LLMs) to improve radiation therapy target volume delineation by integrating image and text data. This multimodal approach enhances accuracy and efficiency in cancer treatment planning.

More Related Videos

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Published on: May 8, 2018

Related Experiment Videos

Last Updated: Jun 9, 2025

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

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Published on: May 8, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Radiation Oncology

Background:

Target volume contouring in radiation therapy is complex, requiring integration of both imaging and clinical text data.
Current AI models often struggle with multimodal data integration for precise target delineation.

Purpose of the Study:

To introduce LLMSeg, an LLM-driven multimodal AI for 3D context-aware target volume delineation in radiation oncology.
To assess LLMSeg's performance in challenging scenarios like breast cancer radiotherapy, external validation, and data-insufficient environments.

Main Methods:

Developed LLMSeg, a multimodal AI leveraging large language models (LLMs) to fuse image and text-based clinical information.
Validated LLMSeg in breast cancer radiotherapy, focusing on external validation and data-scarce conditions.

Main Results:

LLMSeg demonstrated significantly improved performance over conventional unimodal AI models.
The multimodal AI exhibited robust generalization capabilities and data efficiency in validation studies.

Conclusions:

LLM-driven multimodal AI offers a promising approach for enhancing target volume delineation in radiation oncology.
LLMSeg shows potential for real-world clinical application, particularly in data-limited settings.