Peng Jiayuan, Yang Cui, Guo Hongbo, Shen Lijun, Zhang Min, Wang Jiazhou, Zhang Zhen, Cai Bin, Hu Weigang
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, China; Shanghai key laboratory of Radiation Oncology, Shanghai, China.
Department of Radiation Oncology, TengZhou Central People's hospital, Shandong, China.
Comput Methods Programs Biomed. 2023 Apr;231:107263. doi: 10.1016/j.cmpb.2022.107263. Epub 2022 Nov 23.
To establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy.
The RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans.
Overall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D by only 2.33% (p < 0.001) and a decrease in PTV D by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment.
The quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy.
建立并评估一种（准）实时自动治疗计划（RTTP）策略，该策略利用基于非正交卷积运算的一步全三维通量图预测模型进行直肠癌放疗。
RTTP方法根据射束入射方向直接从容积CT和解剖数据中提取三维投影。建立了一种具有非正交卷积运算的三维深度学习模型，该模型将锥束空间中的投影作为输入，沿着并围绕射线追踪路径提取特征，并输出每个射束的预测通量图（PFM）。然后将PFM转换为具有可交付MU的MLC序列，以生成最终治疗计划。共有314例直肠腺癌患者的2198个投影数据样本用于模型训练和验证。另外20例患者用于通过将计划质量、效率、可交付性性能和医生盲审结果与手动计划进行比较来测试RTTP方法的可行性。
总体而言，RTTP计划符合靶区覆盖、适形性、均匀性和危及器官剂量 sparing的临床剂量标准。与手动计划相比，RTTP计划显示PTV D仅增加2.33%（p < 0.001），PTV D减少0.45%（p < 0.05）。RTTP计划显示膀胱剂量增加，V分别为14.01±11.75%和10.74±8.51%，股骨头平均剂量无显著增加。RTTP计划提高了计划效率，通量图生成时间为39秒，而手动计划为944秒；总MU的可交付性性能节省了1.91%（p < 0.001）。根据我们医生的盲法计划审查，55%的RTTP计划可直接用于临床放射治疗。
准RTTP方法在直肠癌放疗中提高了计划效率和可交付性性能，同时保持了与优化后的手动计划相近的计划质量。
