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MR-LINAC-Guided Adaptive Radiotherapy for Gastric MALT: Two Case Reports and a Literature Review

Authors :
Yajun Song
Zhenjiang Li
Huadong Wang
Yun Zhang
Jinbo Yue
Source :
Radiation, Vol 2, Iss 3, Pp 259-267 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

It is still very challenging to use conventional radiation therapy techniques to treat stomach tumors, although image-guided radiotherapy, mainly by kV X-ray imaging techniques, has become routine in the clinic. This is because the stomach is one of the most deformable organs, and thus it is vulnerable to respiratory motions, daily diet, and body position changes. In addition, X-ray radiographs and CT volumetric images have low contrast in soft tissues. In contrast, magnetic resonance imaging (MRI) techniques provide good contrast in images of soft tissues. The emerging MR-guided radiotherapy, based on the MR-LINAC system, may have the potential to solve the above difficulties due to its unique advantages. The real-time imaging feature and the high-contrast of soft tissues MR images provided by the MR-LINAC system have facilitated the therapeutic adaptive planning. Online learning capabilities could be used to optimize the automatic delineation of the target organ or tissue prior to each radiotherapy session. This could greatly improve the accuracy and efficiency of the target delineation in adaptive planning. In this clinical case report, we elaborated a workflow for the diagnosis and treatment of two patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma. One patient underwent MR-guided daily adaptive radiotherapy based on daily automated segmentation using the novel artificial intelligence (AI) technique for gastric delineation.

Details

Language :
English
ISSN :
2673592X and 94285063
Volume :
2
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Radiation
Publication Type :
Academic Journal
Accession number :
edsdoj.82786c29cf64b1d942850636031f918
Document Type :
article
Full Text :
https://doi.org/10.3390/radiation2030019