Research Process~The MRI based cohort data set of nasopharyngeal carcinoma was established, and the data of multiple follow-up time points before and after radiotherapy (including initial diagnosis, 6 months, 12 months and 24 months after radiotherapy) were standardized to obtain the longitudinal data set;~Region of interest (ROI): it mainly delineates the bilateral temporal lobe, brain stem and other brain regions, and extracts the corresponding image features in ROI;~Feature selection: using the strategy of radiomics combined with Artificial Neural Network to reduce the dimension of high-dimensional image features, the key features are selected and used for the subsequent construction of classification and prediction model;~Extracting key features: using vertical axis data analysis method and logistic regression to establish dynamic prediction model.
This project focuses on the early prediction and diagnosis of radiation-induced brain injury in nasopharyngeal carcinoma patients. Based on the big data of imaging and serum metabonomics samples, combined with the machine learning analysis method， dynamic evolution mode of radio-metabolomics characteristics was analyzed . The potential internal relationship between brain structure and serum metabolic changes was explored, and the individualized prediction model was constructed to screen out the high-risk patients with brain injury after tumor radiotherapy, so as to provide reference for the diagnosis of radiation-induced brain injury caused by tumor. radiotherapy Intelligent diagnosis provides a new theoretical and practical basis.