Toward real-time automatic treatment planning (RTTP) with a one-step 3D fluence map prediction method and (nonorthogonal) convolution technique. (April 2023)

• Record Type: Journal Article
• Authors: Peng, Jiayuan; Yang, Cui; Guo, Hongbo; Shen, Lijun; Zhang, Min; Wang, Jiazhou; Zhang, Zhen; Cai, Bin; Hu, Weigang
• Is Part Of: Computer methods and programs in biomedicine. Volume 231(2023)
• Publication Date: 2023-04
• Subjects: Radiotherapy -- Real-time treatment planning -- Autoplanning -- Artificial intelligence -- Adaptive planning -- Nonorthogonal convolution; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107263
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A (quasi) real-time automatic treatment planning (RTTP) method for radiotherapy was proposed. Fluence map prediction took only one step after the anatomy contours were prepared. Model training was based on 314 patients with 2198 projection data samples. Plan quality and physician blinded review results are close to the manual planning method. A promising planning method in which real-time planning efficiency and reasonable dose requirements are needed. Abstract: Purpose: 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. Methods: 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. Results: 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 D1% by only 2.33% ( p < 0.001) and a decrease in PTV D99% by 0.45% ( p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V50 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. Conclusion: 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.