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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Veröffentlicht in:	International journal of radiation oncology, biology, physics Jg. 121; H. 1; S. 230
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	United States 01.01.2025
Schlagworte:	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-Zugang:	Weitere Angaben
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