Hu Jiang, Liu Boji, Xie Weihao, Zhu Jinhan, Yu Xiaoli, Gu Huikuan, Wang Mingli, Wang Yixuan, Qi ZhenYu
Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Sun Yat-sen Memory Hospital, Guangzhou, China.
Front Oncol. 2021 Jan 7;10:551763. doi: 10.3389/fonc.2020.551763. eCollection 2020.
To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.
A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.
With comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.
The automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.
验证基于知识的全自动鼻咽癌(NPC)计划方法的可行性和效率,特别关注提高自动计划成功率的可能途径。
基于99例既往治疗的NPC患者,开发了基于知识的剂量体积直方图(DVH)预测模型,通过该模型为每个头颈部危及器官(OAR)自动生成调强放射治疗(IMRT)计划的优化目标和相应优先级。在17例新的NPC病例中评估了自动知识计划(KBP)方法,并与手动计划(MP)和专家计划(EXP)在靶区剂量覆盖、适形指数(CI)、均匀性指数(HI)以及正常组织保护方面进行比较。为了量化计划质量,应用一种指标进行计划评估。还研究了不同计划者之间计划质量和时间消耗的差异。
在靶区剂量分布相当的情况下,与MP和EXP相比,KBP方法在关键器官如视交叉(p<0.001)、视神经(p=0.021)和颞叶(p<0.001)实现了显著的剂量降低,但在脊髓保护方面失败(p<0.001)。总体计划质量评估中,KBP、MP和EXP的平均得分分别为144.59±11.48、142.71±15.18和144.82±15.17(p=0.259)。在17个KBP计划中,共有15个(即88.24%)被我们的医生判定为临床可接受。
使用DVH预测模型的自动KBP方法为NPC患者在短时间内生成临床可接受的计划提供了一种可能途径。