Esposito Pier Giorgio, Castriconi Roberta, Mangili Paola, Broggi Sara, Fodor Andrei, Pasetti Marcella, Tudda Alessia, Di Muzio Nadia Gisella, Del Vecchio Antonella, Fiorino Claudio
Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
Radiotherapy, San Raffaele Scientific Institute, Milano, Italy.
Phys Imaging Radiat Oncol. 2022 Jun 23;23:54-59. doi: 10.1016/j.phro.2022.06.009. eCollection 2022 Jul.
BACKGROUND/PURPOSE: Tomotherapy may deliver high-quality whole breast irradiation at static angles. The aim of this study was to implement Knowledge-Based (KB) automatic planning for left-sided whole breast using this modality.
MATERIALS/METHODS: Virtual volumetric plans were associated to the dose distributions of 69 Tomotherapy (TT) clinical plans of previously treated patients, aiming to train a KB-model using a commercial tool completely implemented in our treatment planning system. An individually optimized template based on the resulting KB-model was generated for automatic plan optimization. Thirty patients of the training set and ten new patients were considered for internal/external validation. Fully-automatic plans (KB-TT) were generated and compared using the same geometry/number of fields of the corresponding clinical plans.
KB-TT plans were successfully generated in 26/30 and 10/10 patients of the internal/external validation sets; for 4 patients whose original plans used only two fields, the manual insertion of one/two fields before running the automatic template was sufficient to obtain acceptable plans. Concerning internal validation, planning target volume V/D/dose distribution standard deviation improved by 0.9%/0.4Gy/0.2Gy (p < 0.05) against clinical plans; Organs at risk mean doses were also slightly improved (p < 0.05) by 0.07/0.4/0.2/0.01 Gy for left lung/heart/right breast/right lung respectively. Similarly satisfactory results were replicated in the external validation set. The resulting treatment duration was 8 ± 1 min, consistent with our clinical experience. The active planner time per patient was 5-10 minutes.
Automatic TT left-sided breast KB-plans are comparable to or slightly better than clinical plans and can be obtained with limited planner time. The approach is currently under clinical implementation.
背景/目的：断层放疗可以在静态角度下提供高质量的全乳照射。本研究的目的是使用这种方式对左侧全乳实施基于知识（KB）的自动计划。
材料/方法：虚拟容积计划与69例先前治疗患者的断层放疗（TT）临床计划的剂量分布相关联，旨在使用完全集成在我们治疗计划系统中的商业工具训练一个KB模型。基于所得KB模型生成了一个单独优化的模板，用于自动计划优化。考虑将训练集的30例患者和另外10例新患者用于内部/外部验证。生成全自动计划（KB-TT），并使用相应临床计划的相同几何形状/射野数量进行比较。
在内部/外部验证集的26/30例和10/10例患者中成功生成了KB-TT计划；对于4例原始计划仅使用两个射野的患者，在运行自动模板之前手动插入一个/两个射野足以获得可接受的计划。关于内部验证，与临床计划相比，计划靶体积V/D/剂量分布标准差分别提高了0.9%/0.4Gy/0.2Gy（p<0.05）；危及器官的平均剂量也略有改善（p<0.05），左肺/心脏/右乳/右肺分别改善了0.07/0.4/0.2/0.01 Gy。在外部验证集中也得到了类似的满意结果。由此产生的治疗时间为8±1分钟，与我们的临床经验一致。每位患者的主动计划时间为5 - 10分钟。
自动TT左侧乳腺KB计划与临床计划相当或略优于临床计划，并且可以在有限的计划时间内获得。该方法目前正在临床实施中。
