Extracerebral Normalization of 18F-FDG PET Imaging Combined with Behavioral CRS-R Scores Predict Recovery from Disorders of Consciousness.

Abstract

Identifying patients likely to regain consciousness early on is a challenge. The assessment of consciousness levels and the prediction of wakefulness probabilities are facilitated by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET). This study aimed to develop a prognostic model for predicting 1-year postinjury outcomes in prolonged disorders of consciousness (DoC) using 18F-FDG PET alongside clinical behavioral scores. Eighty-seven patients with prolonged DoC newly diagnosed with behavioral Coma Recovery Scale-Revised (CRS-R) scores and 18F-FDG PET/computed tomography (18F-FDG PET/CT) scans were included. PET images were normalized by the cerebellum and extracerebral tissue, respectively. Images were divided into training and independent test sets at a ratio of 5:1. Image-based classification was conducted using the DenseNet121 network, whereas tabular-based deep learning was employed to train depth features extracted from imaging models and behavioral CRS-R scores. The performance of the models was assessed and compared using the McNemar test. Among the 87 patients with DoC who received routine treatments, 52 patients showed recovery of consciousness, whereas 35 did not. The classification of the standardized uptake value ratio by extracerebral tissue model demonstrated a higher specificity and lower sensitivity in predicting consciousness recovery than the classification of the standardized uptake value ratio by cerebellum model. With area under the curve values of 0.751 ± 0.093 and 0.412 ± 0.104 on the test sets, respectively, the difference is not statistically significant (P = 0.73). The combination of standardized uptake value ratio by extracerebral tissue and computed tomography depth features with behavioral CRS-R scores yielded the highest classification accuracy, with area under the curve values of 0.950 ± 0.027 and 0.933 ± 0.015 on the training and test sets, respectively, outperforming any individual mode. In this preliminary study, a multimodal prognostic model based on 18F-FDG PET extracerebral normalization and behavioral CRS-R scores facilitated the prediction of recovery in DoC.