首页> 外文期刊>Journal of applied clinical medical physics / >Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning
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Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning

机译:在基于知识的治疗计划算法中使用立体定向放射疗法(SBRT)开发和评估肺癌患者的临床模型

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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 generated 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 0.4 Gy ± 0.8 Gy , 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 ± 0.4 Gy , p = 0.01. Plans using template beam arrangements met target and OAR criteria, with an increase noted in maximum heart dose ( 1.2 ± 2.2 Gy , 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 locations 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.PACS number(s): 87.55de, 87.55kh, 87.53Ly
机译:这项研究的目的是在基于知识的治疗计划算法中描述针对采用立体定向放射疗法(SBRT)治疗的肺癌患者的临床模型,并评估该模型的性能以及对不同计划技术,肿瘤的适用性位置和光束布置。基于知识的模型(KBM)的开发包括针对我们机构先前接受治疗的105例SBRT肺癌患者的计划。 KBM受IMRT,VMAT和3D CRT技术的组合训练。 IMRT和VMAT的25个案例均验证了模型性能。完整的KBM包括位于中央与周边(43:62),上部与下部(62:43),前部与后部(60:45)的病变。根据肿瘤位置创建四个单独的亚KBM。将结果与完整KBM进行比较,以评估其健壮性。梁模板与优化器结合使用,以评估模型处理次优梁放置的能力。在每个KBM生成的计划之间评估了风险器官(OAR)的剂量差异。就目标一致性和OAR剂量而言,基于知识的计划(KBP)与临床计划相当。与临床计划相比,KBP导致最大脊髓剂量降低1.0±1.6 Gy,p = 0.007。与完整KBM相比,根据肿瘤位置拆分的亚KBM没有产生明显更好的DVH估计值。对于中心病变,与完整KBM相比,外周亚KBM导致食管的剂量降低至0.035 cc和5 cc,两者均为0.4 Gy±0.8 Gy,p = 0.025。对于所有病变,与完整KBM相比,后部KBM产生的脊髓剂量更高,分别为0.035 cc,0.35 cc和1.2 cc,分别为0.2±0.4 Gy,p = 0.01。使用模板波束布置的计划符合目标和OAR标准,相对于计划的KBP,九场计划的最大心脏剂量(1.2±2.2 Gy,p = 0.01)和GI(0.2±0.4,p = 0.01)有所增加自定义光束角度。由多种治疗方式和病变部位组成的基于知识的肺SBRT模型产生的计划质量与临床计划相当。通过适当的培训和验证,可以创建一个健壮的KBM,其中包括IMRT和VMAT技术以及不同的病变位置.PACS编号:87.55de,87.55kh,87.53Ly

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