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Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy.

使用VMAT放疗治疗的晚期肺癌患者的内部基于知识的计划模型的验证。

  • 影响因子:2.12
  • DOI:10.1259/bjr.20190535
  • 作者列表:"Tambe NS","Pires IM","Moore C","Cawthorne C","Beavis AW
  • 发表时间:2020-02-01
Abstract

OBJECTIVES:Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. METHODS:Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. RESULTS:The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. CONCLUSION:Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. ADVANCES IN KNOWLEDGE:In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

摘要

目的: 放疗计划的质量可能有很大的不同,取决于计划者的经验和时间限制。治疗计划的可变性可以通过计算实现和最佳剂量分布之间的差异来评估。如果在不损害目标覆盖率和递送能力的情况下,有进一步的范围来减少器官风险剂量,则所实现的治疗计划可能仍然是次优的。本研究旨在开发一个基于知识的计划 (KBP) 模型,通过预测最小可实现的肺体积剂量指标来降低容积调制arc治疗 (VMAT) 肺计划的变异性。 方法: 使用从 40 个回顾性计划收集的剂量测定和几何数据来开发KBP模型,旨在通过计算残余肺体积与总肺体积的比率来预测最小可实现的肺剂量指标。通过重新规划 40 个计划验证了模型的准确性。使用本地开发的脚本计算计划复杂性度量,并通过测量评估其对治疗递送的影响。 结果: 使用KBP导致所有三个研究剂量学参数V5 、V20 和平均肺剂量的计划变异性显著降低 4.9% (p = 0.007 、 10.8 ~ 5.9%),1.3% (p = 0.038,4.0 ~ 2.7%) 和 0.9 gy gy (p = 0.012,2.5 ~ 1.6Gy)。它还增加了肺的保留,而不损害总体计划质量。该模型的准确性被证明是临床上可接受的。与原始计划相比,计划复杂性增加; 然而,如计划验证测量所证明的,对递送错误的影响在临床上是不重要的。 结论: 我们的VMAT肺计划的内部模型导致了计划变异性的显著降低,同时降低了肺剂量。我们的研究还表明,在临床实施KBP模型之前,治疗递送验证是重要的。 知识进展: 内部KBP模型可以预测接受VMAT治疗的晚期肺癌患者的最小可实现肺剂量-体积约束。该研究表明,计划复杂性可能增加,应在临床实施前进行评估。

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影响因子:6.93
发表时间:2020-01-15
DOI:10.1002/ijc.32532
作者列表:["Hata A","Nakajima T","Matsusaka K","Fukuyo M","Morimoto J","Yamamoto T","Sakairi Y","Rahmutulla B","Ota S","Wada H","Suzuki H","Matsubara H","Yoshino I","Kaneda A"]

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作者列表:["Zhang L","Yang Y","Chai L","Bu H","Yang Y","Huang H","Ran J","Zhu Y","Li L","Chen F","Li W"]

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肺肿瘤方向

肺肿瘤,又叫支气管肺癌,是常见的恶性肿瘤之一。肺肿瘤的治疗为包括手术、中药、放疗、化疗及免疫等多学科的综合治疗。

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