A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy
A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy
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DOI：
10.1186/s13014-020-01626-z
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发表时间：
2020-08-03
影响因子：
3.6
通讯作者：
Chen, Chuanben
中科院分区：
文献类型：
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作者：
Bai, Penggang;Weng, Xing;Chen, Chuanben
背景探讨基于知识的自动调强放射治疗（IMRT）计划技术在局部晚期鼻咽癌（NPC）放疗中的可行性。方法回顾性分析140例采用步进式调强放射治疗的鼻咽癌患者，将其分为知识库（n = 115）和测试库（n = 25）。对于知识库中的每个患者，从手动生成的计划中提取重叠体积直方图（OVH）、靶体积直方图（TVH）和剂量目标。5-进行折叠交叉验证以将知识库中的患者分成5组，然后通过使用其他4组来训练每个神经网络（NN）机器学习模型来验证一组。对于测试库中的患者，其OVH和TVH随后由训练模型用于预测相应的一组平均剂量目标，随后用于通过内部开发的自动脚本系统在Pinnacle计划系统中生成自动计划（AP）。所有AP都是在一个单一的优化步骤后获得的。测试患者的手动计划（MP）由经验丰富的医学物理学家严格按照既定的临床方案生成。AP和MP的质量由主治放射肿瘤学家进行评估。采用Mann-Whitney U检验和Bonferroni校正对计划靶区（PTV）覆盖和危及器官（OAR）保留的剂量学参数进行了定量测量和比较。结果试验组中80%以上的患者（19/25）的AP和MP评分相同。AP和MP均达到PTV覆盖标准，不低于80%的患者。就每一项OAR而言，达到其标准的AP数量与MP中的数量相似。AP方法通过将计划持续时间大大减少到MP的约17%（9.85 +/- 1.13 min vs. 57.10 +/- 6.35 min）来提高计划效率。结论为局部晚期鼻咽癌的调强放射治疗提供了一种可靠、有效的基于知识的治疗计划技术。患者特异性剂量目标可以通过基于个体的OVH和临床TVH目标的训练的NN模型来预测。自动规划脚本可以使用这些剂量目标来有效地生成AP，大大缩短了规划时间。与我们诊所的手动计划相比，这些AP具有相当的剂量测定质量。
Background To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy. Methods One hundred forty NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and separated into a knowledge library (n = 115) and a test library (n = 25). For each patient in the knowledge library, the overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the manually generated plan. 5-fold cross validation was performed to divide the patients in the knowledge library into 5 groups before validating one group by using the other 4 groups to train each neural network (NN) machine learning models. For patients in the test library, their OVH and TVH were then used by the trained models to predict a corresponding set of mean dose objectives, which were subsequently used to generate automated plans (APs) in Pinnacle planning system via an in-house developed automated scripting system. All APs were obtained after a single step of optimization. Manual plans (MPs) for the test patients were generated by an experienced medical physicist strictly following the established clinical protocols. The qualities of the APs and MPs were evaluated by an attending radiation oncologist. The dosimetric parameters for planning target volume (PTV) coverage and the organs-at-risk (OAR) sparing were also quantitatively measured and compared using Mann-Whitney U test and Bonferroni correction. Results APs and MPs had the same rating for more than 80% of the patients (19 out of 25) in the test group. Both AP and MP achieved PTV coverage criteria for no less than 80% of the patients. For each OAR, the number of APs achieving its criterion was similar to that in the MPs. The AP approach improved planning efficiency by greatly reducing the planning duration to about 17% of the MP (9.85 +/- 1.13 min vs. 57.10 +/- 6.35 min). Conclusion A robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific dose objectives can be predicted by trained NN models based on the individual's OVH and clinical TVH goals. The automated planning scripts can use these dose objectives to efficiently generate APs with largely shortened planning time. These APs had comparable dosimetric qualities when compared to our clinic's manual plans.