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

Wenwen Cai; Shouliang Ding; Huali Li; Xuanru Zhou; Wen Dou; Linghong Zhou; Ting Song; Yongbao Li

doi:10.1186/s13014-024-02401-0

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

Radiat Oncol. 2024 Mar 20;19(1):39. doi: 10.1186/s13014-024-02401-0.

Authors

Wenwen Cai^#¹, Shouliang Ding^#², Huali Li¹, Xuanru Zhou¹, Wen Dou³, Linghong Zhou¹, Ting Song⁴, Yongbao Li⁵

Affiliations

¹ School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
² Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China.
³ Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
⁴ School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China. tingsong2015@smu.edu.cn.
⁵ Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China. liyb1@sysucc.org.cn.

^# Contributed equally.

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 D_max of the brainstem and spinal cord, and D_mean of the left and right parotid glands significantly decreased (P < 0.05).

Conclusion: We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

Keywords: Automatic planning; Fluence prediction; IMRT; Nasopharyngeal carcinoma; Plan fine-tuning.

MeSH terms

Humans
Nasopharyngeal Carcinoma / radiotherapy
Nasopharyngeal Neoplasms* / radiotherapy
Organs at Risk
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted / methods
Radiotherapy, Intensity-Modulated* / methods

Abstract

MeSH terms

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