Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning
Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning
复制标题
通过静态场通量预测的自动IMRT计划（AIP-SFFP）：用于实时前列腺治疗计划的深度学习算法
DOI：
10.1088/1361-6560/aba5eb
复制
发表时间：
2020-09-07
影响因子：
3.5
通讯作者：
Wang, Chunhao
中科院分区：
文献类型：
--
作者：
Li, Xinyi;Zhang, Jiahan;Wang, Chunhao
这项工作的目的是开发一种基于深度学习（DL）的算法，通过静态场通量预测（AIP-SFFP）进行自动调强放射治疗（IMRT）规划，以实现实时规划效率的自动前列腺IMRT规划。采用以下方法：AIP-SFFP通过使用患者解剖结构预测注量图来生成前列腺IMRT计划。不需要逆向规划。AIP-SFFP以定制的深度学习（DL）神经网络为中心，用于通量图预测。将预测导入商业治疗计划系统，用于剂量计算和计划生成。AIP-SFFP已被证明可用于前列腺IMRT同时整合加强计划（28 fx中58.8戈伊/70戈伊至PTV 58.8戈伊/PTV（70戈伊），PTV =计划目标体积）。使用基于知识的规划（KBP）计划生成器从106名患者生成训练数据。设计了两种二维投影图像来表示结构的大小和位置，总共使用了八种投影来描述目标和危险器官。九个模板光束角度的投影被堆叠作为人工智能（AI）训练的输入。14例患者被用作独立测试。将生成的测试计划与KBP培训计划生成器和临床实践的计划进行比较。获得以下结果：标准化后（PTV 70 Gy/70戈伊= 95%），所有14个AI计划均符合机构标准。AI计划中PTV的覆盖范围（58.8戈伊）与KBP和诊所计划相当，但无统计学意义。AI计划的全身（BODY）D1cc和直肠D0.1cc略高于（
The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency. The following method was adopted: AIP-SFFP generates a prostate IMRT plan through predictions of fluence maps using patient anatomy. No inverse planning is required. AIP-SFFP is centered on a custom-built deep learning (DL) neural network for fluence map prediction. Predictions are imported to a commercial treatment-planning system for dose calculation and plan generation. AIP-SFFP was demonstrated for prostate IMRT simultaneously-integrated-boost planning (58.8 Gy/70 Gy to PTV58.8 Gy/PTV(70 Gy)in 28 fx, PTV = Planning Target Volume). Training data was generated from 106 patients using a knowledge-based planning (KBP) plan generator. Two types of 2D projection images were designed to represent structures' sizes and locations, and a total of eight projections were utilized to describe targets and organs-at-risk. Projections at nine template beam angles were stacked as inputs for artificial intelligence (AI) training. 14 patients were used as independent tests. The generated test plans were compared with the plans from the KBP training plan generator and clinic practice. The following results were obtained: After normalization (PTV70 GyV70 Gy= 95%), all 14 AI plans met institutional criteria. The coverage of PTV(58.8 Gy)in the AI plans was comparable to KBP and clinic plans without statistical significance. The whole body (BODY) D1cc and rectum D0.1cc of AI plans were slightly higher (