Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA.
J Appl Clin Med Phys. 2021 Jan;22(1):109-116. doi: 10.1002/acm2.13114. Epub 2020 Dec 3.
To develop a knowledge-based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early-stage non-small-cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)-based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlan software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small-field dosimetry errors associated with treatment delivery.
KBP model was developed and validated using 70 clinically treated high quality non-coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k-DCA planning routine was deployed to create plans incorporating historical three-dimensional-conformal SBRT planning practices via DCA-based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k-DCA plans were compared with clinically treated plans per RTOG-0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall-off and organs-at-risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded.
KBPs and k-DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1-62.3 cc). KBPs and k-DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k-DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k-DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k-DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k-DCA plans showed better agreement with planned dose distribution.
A k-DCA planning routine was developed in concurrence with a knowledge-based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model-based KBP techniques and improve plan quality and treatment planning efficiency.
通过使用商业 RapidPlan 软件，为基于动态适形弧（DCA）的容积调强弧形治疗（VMAT）的外周早期非小细胞肺癌（NSCLC）肿瘤的立体定向体放射治疗（SBRT）开发基于知识的计划（KBP）程序。这种方法可以提高计划质量、降低复杂性并最小化与治疗交付相关的相互作用效应和小场剂量学误差。
使用 70 个临床治疗的高质量非共面 VMAT 肺部 SBRT 计划进行培训，并使用 20 个独立计划进行验证，来开发和验证 KBP 模型。所有患者均接受 54 Gy 的三次治疗。此外，还部署了一种新的 k-DCA 计划程序，以通过 DCA 方法在自动化计划引擎中进行 VMAT 优化之前，将历史上的三维适形 SBRT 计划实践纳入计划中。根据 RTOG-0618 的要求，比较了常规 KBP 和 k-DCA 计划与临床治疗计划在靶区适形性、肿瘤剂量异质性、中间剂量下降和危及器官（OAR）保护方面的差异。记录了治疗计划时间、治疗交付效率和准确性。
KBP 和 k-DCA 计划与临床计划相似或优于临床计划。验证的平均计划靶区体积为 22.4±14.1 cc（7.1-62.3 cc）。KBP 和 k-DCA 计划提供了与临床计划相似的一致性，平均绝对差异分别为 0.01 和 0.01。KBP 和 k-DCA 计划都降低了 OAR 的最大剂量。KBP 平均增加了 1316 个监测单位（MU）（P<0.001），而 k-DCA 平均减少了 1114 个 MU（P<0.001）。该程序可以在不到 30 分钟的时间内创建 k-DCA 计划。独立的蒙特卡罗计算表明，k-DCA 计划与计划剂量分布的吻合度更好。
为外周肺肿瘤的治疗开发了一种与基于知识的方法一致的 k-DCA 计划程序。该方法最小化了与基于模型的 KBP 技术相关的计划复杂性，并提高了计划质量和治疗计划效率。
