Cancer risk assessment in modern radiotherapy workflow with medical big data

Fu Jin,1 Huan-Li Luo,1 Juan Zhou,2 Ya-Nan He,1 Xian-Feng Liu,1 Ming-Song Zhong,1 Han Yang,1 Chao Li,1 Qi-Cheng Li,1 Xia Huang,1 Xiu-Mei Tian,1 Da Qiu,1 Guang-Lei He,1 Li Yin,1 Ying Wang1 1Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, Chongqing Cancer Hospital, Chongqing, People’s Republic of China; 2Forensic Identification Center, College of Criminal Investigation, Southwest University of Political Science and Law, Chongqing, People’s Republic of China Abstract: Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose–response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI. Keywords: cancer risk, radiotherapy, workflow, big data&nbsp