Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy

Strahlentherapie und Onkologie https://doi.org/10.1007/s00066-023-02126-1 ORIGINAL ARTICLE Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy Yan Shao1 · Jindong Guo1 · Jiyong Wang2 · Ying Huang1 · Wutian Gan3 · Xiaoying Zhang4 · Ge Wu5 · Dong Sun6 · Yu Gu7 · Qingtao Gu8 · Ning Jeff Yue9 · Guanli Yang10 · Guotong Xie5,11,12 · Zhiyong Xu1 Received: 28 September 2022 / Accepted: 10 July 2023 © The Author(s) 2023 Abstract Purpose The goal of this study was to propose a knowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT). Methods and materials From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct a planning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by a firewall. One was the in-hospital workstation, and the other was the search engine in the cloud. Based on our previous study, A-Net in the in-hospital workstation was used to generate predicted virtual dose images. A search engine including a three-dimensional convolutional neural network (3D CNN) was constructed to derive the feature vectors of dose images. By comparing the similarity of the features between virtual dose images and the clinical dose images in the database, the most similar feature was found. The optimization parameters (OPs) of the treatment plan corresponding to the most similar feature were assigned to the new plan, and the design of a new treatment plan was automatically completed. After αDiar was developed, we performed two studies. The first retrospective study was conducted to validate whether this architecture was qualified for clinical practice and involved 96 patients. The second comparative study was performed to investigate whether αDiar could assist dosimetrists in improving the quality of planning for the patients. Two dosimetrists were involved and designed plans for only one trial with and without αDiar; 26 patients were involved in this study. Results The first study showed that about 54% (52/96) of the automatically generated plans would achieve the dosimetric constraints of the Radiation Therapy Oncology Group (RTOG) and about 93% (89/96) of the automatically generated plans would achieve the dosimetric constraints of the National Comprehensive Cancer Network (NCCN). The second study showed that the quality of treatment planning designed by junior dosimetrists was improved with the help of αDiar. Authors Y. Shao, J. Guo and J. Wang contributed equally to the manuscript. Author Responsible for Statistical Analysis Yan Shao Availability of data Research data are not available at this time.  Guanli Yang  Guotong Xie  Zhiyong Xu 4 School of Information Science and Engineering, Xiamen University, Xiamen, China 5 Ping An Healthcare Technology Co. Ltd., Shanghai, China 6 School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China 7 School of Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China 8 School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China 9 Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA 1 Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China 10 Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong University, Jinan, China 2 Shanghai Pulse Medical Technology Inc., Shanghai, China 11 Ping An Health Cloud Company Limited, Shanghai, China School of Physics and Technology, University of Wuhan, Wuhan, China 12 Ping An International Smart City Technology Co., Ltd., Shanghai, China 3 K Strahlentherapie und Onkologie Conclusions Our results showed that αDiar was an effective tool to improve planning quality. Over half of the patients’ plans could be designed automatically. For the remaining patients, although the automatically designed plans did not fully meet the clinical requirements, their quality was also better than that of manual plans. Keywords Deep learning · Dose prediction · Knowledge-based planning · Lung cancer · Image retrieval Introduction Artificial intelligence (AI) has been applied in medical imaging-based diagnosis and prognosis and has shown significant advantages with regard to application [1–5]. Although recent work has demonstrated the effectiveness of AI in radiotherapy [6], e.g., AI segmentation of planning target volume (PTV) and organs at risk (OARs) [7–10] and the AI prediction of dose images [11, 12], its application is still limited. In clinical practice, a radiotherapy treatment plan is generated by configuring prescription, optimization algorithm, dose calculation algorithm and grid resolution, settings and options of the optimizer and optimization parameters (OPs) including field geometry, number of fields, and optimization goals. Then, the planner iteratively modifies OPs until the plan meets the clinical constraints. This is a very timeconsuming and laborious process. And to some extent, the selection of the OPs and the plan modification process are based on the planner’s experience [13]. Thus, the quality of radiotherapy treatment plans may vary between planners, and some patients may be treated with suboptimal plans [14, 15]. In order to minimize the variations of plan quality between planners and improve the plan quality, automatic treatment planning (ATP) methods [16–18] were developed. Some commercial ATPs are available, such as RapidPlan [19] and HyperArc [20] from Varian, auto-planning [21] from Philips, RayStation autoplanning modules, EZfluence [22] from Radformation, and Elements [23] from Brainlab. Some other noncommercial systems were also developed by researchers, such as iCycle [24, 25] which utilized an a priori approach with multicriteria optimization. However, all these commercial and noncommercial systems still require planners to select OPs as the input to generate a treatment plan. However, because the selection of OPs depends largely on the experience of planners, suboptimal plans may result. Further work is needed to mitigate the dependence on planners and to generate invariant treatment plans of high quality. In order to solve the above problems, researchers proposed several solutions to improve the quality, uniformity and processing efficiency of planning, such as the use of a complicated objective function, the use of multiobjective optimization and the introduction of knowledge-based automated planning methods (KBAP) [26–29]. Zhang et al. [30] K proposed a semi-automatic radiotherapy treatment planning process by combining the ideas of machine learning automated planning and multicriteria optimization (MCO). In their workflow, handcrafted fe (...truncated)