Fogliata Antonella, Belosi Francesca, Clivio Alessandro, Navarria Piera, Nicolini Giorgia, Scorsetti Marta, Vanetti Eugenio, Cozzi Luca
Oncology Institute of Southern Switzerland, Bellinzona, Switzerland.
Oncology Institute of Southern Switzerland, Bellinzona, Switzerland.
Radiother Oncol. 2014 Dec;113(3):385-91. doi: 10.1016/j.radonc.2014.11.009. Epub 2014 Nov 21.
To evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy applied to advanced lung cancer and to low risk prostate carcinoma patients.
Two sets each of 27 previously treated patients, were selected to train models for the prediction of dose-volume constraints. The models were validated on the same sets of plans (closed-loop) and on further two sets each of 25 patients not used for the training (open-loop).
Quantitative improvements (statistically significant for the majority of the analysed dose-volume parameters) were observed between the benchmark and the test plans. In the pass-fail analysis, the rate of criteria not fulfilled was reduced in the lung patient group from 11% to 7% in the closed-loop and from 13% to 10% in the open-loop studies; in the prostate patient group it was reduced from 4% to 3% in the open-loop study.
Plans were optimised using a knowledge-based model to determine the dose-volume constraints. The results showed dosimetric improvements when compared to the benchmark data, particularly in the sparing of organs at risk. The data suggest that the new engine is reliable and could encourage its application to clinical practice.
评估基于模型的优化过程在容积调强弧形放疗应用于晚期肺癌和低风险前列腺癌患者时的性能。
选择两组各27例先前接受治疗的患者，用于训练预测剂量体积约束的模型。这些模型在相同的计划集（闭环）以及另外两组各25例未用于训练的患者（开环）上进行验证。
在基准计划和测试计划之间观察到了定量改善（对于大多数分析的剂量体积参数具有统计学意义）。在通过/失败分析中，肺癌患者组中未满足标准的比例在闭环研究中从11%降至7%，在开环研究中从13%降至10%；在前列腺癌患者组中，开环研究中该比例从4%降至3%。
使用基于知识的模型来确定剂量体积约束以优化计划。结果表明，与基准数据相比，剂量学有改善，尤其是在危及器官的 sparing方面。数据表明新引擎可靠，并可鼓励其应用于临床实践。
