A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers. (February 2023)

• Record Type: Journal Article
• Authors: Zhou, Yin; Luo, Binghui; Sang, Jiugao; Li, Cheng; Zhu, Meng; Zhu, Zhengfei; Dai, Jianrong; Wang, Jianhua; Chen, Haibo; Zhai, Shuwei; Lu, Lina; Liu, Hui; Yu, Genhua; Ye, Jin; Zhang, Zhen; Huan, Jian
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Cloud-based system -- Radiotherapy -- Radiation treatment planning -- Remote services -- Distributed and parallel computing -- Consultation -- Collaboration -- Decision support -- Community radiotherapy centers -- OncoEvidance®; 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.2022.107270
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The system saves cost and time for both consultants and community radiotherapy centers. The system is ubiquitously accessible in an ad hoc way. The system utilized distributed and paralleling computing. The online operations are streamlined efficiently due to the deep integration of comprehensive technical cores of radiotherapy planning. Help the under-staffed community radiotherapy centers acquire decision support as demanded and elevate the quality of radiation treatment. Abstract: Purpose: This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system. Methods and Materials: A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely. Results: All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification. Conclusion: The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.