Biomarker Importance Analysis for Autoimmune Encephalitis with PET

Autoimmune encephalitis (AE), particularly Leucine-rich Glioma-Inactivated-1 autoimmune encephalitis (LGII-AE), presents complex diagnostic challenges. The brain regions and extracted features used in previous machine learning works are not fine-grained. This study aims to bridge these gaps by enhancing the granularity of the radiomic analysis, and we encoded a total of 165 brain regions using 107 radiomic features. To further investigate the pathogenesis of facio brachial dystonic seizures (FBDS), a typical clinical symptoms of LGII-AE, three tasks are designed: identifying CDLGII-AE, @FBDS subgroup and @non-FBDS subgroup from healthy control (HC) group. We employed the Mann-Whitney U-test and a Random Forest (RF) algorithm to evaluate feature significance comprehensively. Furthermore, we proposed a feature selection and ensemble learning method for classification, achieving consistency ratios with the average standardized uptake value ratio (SUVr) results of 85 %, 60 %, and 80 % respectively. Our findings indicate that morphological and first-order features are significant biomarkers. The temporal pole middle temporal gyrus_R, inferior temporal gyrus_R, middle temporal gyrus_L and superior frontal gyrus,dorsolateral_R may be linked to LGII-AE. Additionally, certain areas in the cerebellum, frontal and hippocampus regions could be crucial in FBDS. Our study provides deeper insights into LGII-AE's pathogenesis and offers a more comprehensive framework for future research into its diagnosis.