Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning

Artificial intelligence-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiation therapy target volume. Our goal was to model the delineation of target volume as a clinical decision-making problem...

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Bibliographic Details

Published in:	International journal of radiation oncology, biology, physics Vol. 121; no. 1; p. 230
Main Authors:	Rajendran, Praveenbalaji, Chen, Yizheng, Qiu, Liang, Niedermayr, Thomas, Liu, Wu, Buyyounouski, Mark, Bagshaw, Hilary, Han, Bin, Yang, Yong, Kovalchuk, Nataliya, Gu, Xuejun, Hancock, Steven, Xing, Lei, Dai, Xianjin
Format:	Journal Article
Language:	English
Published:	United States 01.01.2025
Subjects:	Algorithms Artificial Intelligence Humans Male Oropharyngeal Neoplasms - radiotherapy Prostatic Neoplasms - diagnostic imaging Prostatic Neoplasms - radiotherapy Radiotherapy Planning, Computer-Assisted - methods Tumor Burden
ISSN:	1879-355X, 1879-355X
Online Access:	Get more information
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