Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma

Autor: Wenwen Cai, Shouliang Ding, Huali Li, Xuanru Zhou, Wen Dou, Linghong Zhou, Ting Song, Yongbao Li
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Radiation Oncology, Vol 19, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1748-717X
DOI: 10.1186/s13014-024-02401-0
Popis: Abstract Background At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. Methods A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. Results The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P
Databáze: Directory of Open Access Journals
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