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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Artículo Open Access |
| Published: |
Wiley
2025
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1111/cns.70513 |
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Table of Contents:
- Diagnosis and Subtyping of Autoimmune Encephalitis Using an Attention‐Based Multi‐Instance Learning Model: A Multi‐Center 18F‐FDG PET Study Yueqian Sun Ruizhe Sun Jiahua Lv Qingxia Kong Cixiang Dai Bin Wang Xiong Han Min Chen Ruihan Liu Yan Jiang Leilei Yuan Lin Ai Xiaodong Yang Yiqiang Chen Qun Wang CNS Neuroscience & Therapeutics ABSTRACTBackgroundThe aim was to develop an attention‐based model using 18F‐fluorodeoxyglucose (18F‐FDG) PET imaging to differentiate autoimmune encephalitis (AE) patients from controls and to discriminate among different AE subtypes.MethodsThis multi‐center retrospective study enrolled 390 participants: 222 definite AE patients (comprising four subtypes: LGI1‐AE, NMDAR‐AE, GABAB‐AE, GAD65‐AE), 122 age‐ and sex‐matched healthy controls, and 33 age‐ and sex‐matched antibody‐negative AE patients along with 13 age‐ and sex‐matched viral encephalitis patients, both serving as disease controls. An attention‐based multi‐instance learning (MIL) model was trained using data from one hospital and underwent external validation with data from other institutions. Additionally, a multi‐modal MIL (m‐MIL) model integrating imaging features, age, and sex parameters was evaluated alongside logistic regression (LR) and random forest (RF) models for comparative analysis.ResultsThe attention‐based m‐MIL model outperformed classical algorithms (LR, RF) and single‐modal MIL in AE vs. all controls binary classification, achieving the highest accuracy (84.00% internal, 67.38% external) and sensitivity (90.91% internal, 71.19% external). For multiclass AE subtype classification, the MIL‐based model achieved 95.05% (internal) and 77.97% (external) accuracy. Heatmap analysis revealed that NMDAR‐AE involved broader brain regions, including the medial temporal lobe (MTL) and basal ganglia (BG), whereas LGI1‐AE and GABAB‐AE showed focal attention on the MTL and BG. In contrast, GAD65‐AE demonstrated concentrated attention exclusively in the MTL.ConclusionThe m‐MIL model effectively discriminates AE patients from controls and enables subtyping of different AE subtypes, offering a valuable diagnostic tool for the clinical assessment and classification of AE. 10.1111/cns.70513 http://creativecommons.org/licenses/by/4.0/