Deep Longitudinal Feature Representations for Detection of Postradiotherapy Brain Injury at Presymptomatic Stage

Temporal lobe injury (TLI), a form of nervous system damage in the brain, is a major neurological complication after radiation therapy (RT). TLI must be highly valued because of the irreversible brain injury. This article aims to develop a predictive pipeline, called deep longitudinal feature representations (DLFR), to detect TLI at the presymptomatic stage accurately via the learning of effective deep longitudinal feature representations. DLFR characterizes high-level information and developmental changes within and across subjects The DLFR consists of four components: (i) extraction of deep features from a pretrained ResNet50 model; (ii) compression of learned highly representative features by the global max pooling; (iii) fusion of deep longitudinal features for the fully use of all follow-up data; (iv) random forest-based prediction of the diagnostic status. In total, 244 nasopharyngeal carcinoma patients before and after RT with a follow-up period of 0 ~ 9 years were included for analysis. All patients were divided into four different latency groups, and the current latency was used for training to predict the diagnostic status of the next latency. The AUCs of the predicted three different latency groups using DLFR were 0.64 ± 0.11, 0.76 ± 0.10, and 0.88 ± 0.05, while those of radiomics features were 0.56 ± 0.06, 0.63 ± 0.03, and 0.53 ± 0.04, and those of histogram of oriented gradients features were 0.60 ± 0.09, 0.52 ± 0.03, and 0.58 ± 0.06. Most importantly, the AUCs of the predicted three different latency groups for white matter regions were 0.66 ± 0.10, 0.80 ± 0.09, and 0.78 ± 0.09. Our proposed method can dynamically detect TLI at the presymptomatic stage, which can enable the administration of preventive neurological intervention.