Rago Maria, Placidi Lorenzo, Polsoni Mattia, Rambaldi Giulia, Cusumano Davide, Greco Francesca, Indovina Luca, Menna Sebastiano, Placidi Elisa, Stimato Gerardina, Teodoli Stefania, Mattiucci Gian Carlo, Chiesa Silvia, Marazzi Fabio, Masiello Valeria, Valentini Vincenzo, De Spirito Marco, Azario Luigi
Università Cattolica del Sacro Cuore, Rome, Italy.
Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
PLoS One. 2021 Jan 15;16(1):e0245305. doi: 10.1371/journal.pone.0245305. eCollection 2021.
To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions.
Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures.
All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average.
All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.
评估十一种基于知识（KB）的模型在容积调强弧形放疗（VMAT）计划优化（Varian公司的RapidPlantm（RP））中的性能，该放疗应用于包括淋巴结站、内乳和/或锁骨上区域的全乳综合治疗。
基于120个具有不同标准的VMAT计划数据集生成并训练了六个RP模型。勾画了两个额外结构：用于优化的计划靶体积（PTV）和环形结构。在无需这些结构的情况下创建了另外五个模型，它们是先前模型的孪生模型。
所有模型均在40名患者的独立队列中成功验证，其中30名来自提供训练患者的同一机构，10名来自另一个机构，所得计划与临床计划相比质量相同或更好。内部验证表明，这些模型平均将心脏最大剂量降低约2 Gy，平均剂量降低约1 Gy，V20Gy降低1.5 Gy。与其他模型相比，模型R和L以及没有优化结构的模型B在20%的值中确保了最佳结果。外部验证观察到，RP计划中肺脏的V5Gy平均改善至少16%。心脏平均剂量以及肺脏同侧的V20Gy几乎减半。这些模型平均将脊髓管的最大剂量降低超过2 Gy。
正如我们在内部和外部验证中所见，所有KB模型都能实现均匀的计划质量并带来一些剂量学上的获益。通过将左右乳腺病例分开或仅包括带有局部区域淋巴结的全乳而开发的子KB模型表现良好，与通用模型相当但略差。最后，在没有优化结构的情况下生成的模型比原始模型表现更好。
