Cloud Computing-Assisted Dose Verification System and Method for Tumor Pain Treatment

• Main Authors: Jing Lin, Peng Yang, Kailong Wang, Xuemei Jiang, Yongxiang Zhao, Dongming Lu, Wei Gan, Wenyan Liao, Qiaoming Mo, Zhenbao Gan, Zhangsong Nong, Mingchen Ma, Fengshi He, Jun Pang, Hongliang Tang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cloud computing; tumor treatment; modern medicine; radiation dose
• Online Access: https://ieeexplore.ieee.org/document/9123606/

At present, modern medical data processing systems based on cloud computing, as a very important part of the auxiliary tumor pain treatment program, have played a key role in the soft services of the entire tumor pain treatment program. This article aims at the radiation dose verification problem of tumor patients in tumor treatment. The modern medical data processing system constructed by cloud computing technology is used to solve this kind of problem. The method used in this article is to select 100 tumor patients and use cloud computing-based oncology medical data processing system to analyze patients of different ages and body types, common irradiation sites and irradiation angles, and other irradiation parameters, and calculate using Monte Carlo method. Interventional treatment of the patient's various organs and tissues and the combined results into a large database. The experimental results show that the construction and application of a tumor medical data processing system based on cloud computing can better combine the individual condition and disease characteristics of tumor patients, give an accurate radiation dose for tumor pain treatment, and reduce the incidence of risk events. Compared with the experimental and simulation data of related literature, the results show that the cloud computing-based tumor medical data processing system proposed in this paper has improved the standardization of tumor pain treatment by nearly 15% on average, and the treatment plan satisfaction has increased by nearly 20%.