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在基于知识的治疗计划算法中,使用立体定向体部放疗(SBRT)对肺癌患者进行临床模型的开发与评估。

Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge-based algorithm for treatment planning.

作者信息

Chin Snyder Karen, Kim Jinkoo, Reding Anne, Fraser Corey, Gordon James, Ajlouni Munther, Movsas Benjamin, Chetty Indrin J

机构信息

Henry Ford Health System.

出版信息

J Appl Clin Med Phys. 2016 Nov 8;17(6):263-275. doi: 10.1120/jacmp.v17i6.6429.

Abstract

The purpose of this study was to describe the development of a clinical model for lung cancer patients treated with stereotactic body radiotherapy (SBRT) within a knowledge-based algorithm for treatment planning, and to evaluate the model performance and applicability to different planning techniques, tumor locations, and beam arrangements. 105 SBRT plans for lung cancer patients previously treated at our institution were included in the development of the knowledge-based model (KBM). The KBM was trained with a combination of IMRT, VMAT, and 3D CRT techniques. Model performance was validated with 25 cases, for both IMRT and VMAT. The full KBM encompassed lesions located centrally vs. peripherally (43:62), upper vs. lower (62:43), and anterior vs. posterior (60:45). Four separate sub-KBMs were created based on tumor location. Results were compared with the full KBM to evaluate its robustness. Beam templates were used in conjunction with the optimizer to evaluate the model's ability to handle suboptimal beam placements. Dose differences to organs-at-risk (OAR) were evaluated between the plans gener-ated by each KBM. Knowledge-based plans (KBPs) were comparable to clinical plans with respect to target conformity and OAR doses. The KBPs resulted in a lower maximum spinal cord dose by 1.0 ± 1.6 Gy compared to clinical plans, p = 0.007. Sub-KBMs split according to tumor location did not produce significantly better DVH estimates compared to the full KBM. For central lesions, compared to the full KBM, the peripheral sub-KBM resulted in lower dose to 0.035 cc and 5 cc of the esophagus, both by 0.4Gy ± 0.8Gy, p = 0.025. For all lesions, compared to the full KBM, the posterior sub-KBM resulted in higher dose to 0.035 cc, 0.35 cc, and 1.2 cc of the spinal cord by 0.2 ± 0.4Gy, p = 0.01. Plans using template beam arrangements met target and OAR criteria, with an increase noted in maximum heart dose (1.2 ± 2.2Gy, p = 0.01) and GI (0.2 ± 0.4, p = 0.01) for the nine-field plans relative to KBPs planned with custom beam angles. A knowledge-based model for lung SBRT consisting of multiple treatment modalities and lesion loca-tions produced comparable plan quality to clinical plans. With proper training and validation, a robust KBM can be created that encompasses both IMRT and VMAT techniques, as well as different lesion locations.

摘要

本研究的目的是描述在基于知识的治疗计划算法中,用于接受立体定向体部放疗(SBRT)的肺癌患者的临床模型的开发,并评估该模型的性能以及对不同治疗计划技术、肿瘤位置和射束排列的适用性。我们机构之前治疗的105例肺癌患者的SBRT计划被纳入基于知识的模型(KBM)的开发。KBM采用调强放疗(IMRT)、容积调强弧形放疗(VMAT)和三维适形放疗(3D CRT)技术相结合进行训练。使用25例病例对IMRT和VMAT的模型性能进行验证。完整的KBM涵盖了位于中央与外周(43:62)、上部与下部(62:43)以及前部与后部(60:45)的病变。根据肿瘤位置创建了四个单独的子KBM。将结果与完整的KBM进行比较以评估其稳健性。射束模板与优化器结合使用,以评估模型处理次优射束放置的能力。评估每个KBM生成的计划之间危及器官(OAR)的剂量差异。基于知识的计划(KBP)在靶区适形度和OAR剂量方面与临床计划相当。与临床计划相比,KBP使脊髓最大剂量降低了1.0±1.6 Gy,p = 0.007。与完整的KBM相比,根据肿瘤位置划分的子KBM并未产生明显更好的剂量体积直方图(DVH)估计值。对于中央病变,与完整的KBM相比,外周子KBM使食管0.035 cc和5 cc体积处的剂量降低了0.4 Gy±0.8 Gy,p = 0.025。对于所有病变,与完整的KBM相比,后部子KBM使脊髓0.035 cc、0.35 cc和1.2 cc体积处的剂量增加了0.2±0.4 Gy,p = 0.01。使用模板射束排列的计划符合靶区和OAR标准,相对于采用定制射束角度规划的KBP,九野计划的最大心脏剂量增加(1.2±2.2 Gy,p = 0.01),胃肠道剂量增加(0.2±0.4,p = 0.01)。一个由多种治疗方式和病变位置组成的基于知识的肺癌SBRT模型产生的计划质量与临床计划相当。通过适当的训练和验证,可以创建一个稳健的KBM,它涵盖IMRT和VMAT技术以及不同的病变位置。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ee/5690505/9c806806f41c/ACM2-17-263-g001.jpg

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