AUTHOR=Pan Jian , Lv Ruijuan , Zhou Guifei , Si Run , Wang Qun , Zhao Xiaobin , Liu Jiangang , Ai Lin 

TITLE=The Detection of Invisible Abnormal Metabolism in the FDG-PET Images of Patients With Anti-LGI1 Encephalitis by Machine Learning

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.812439

DOI=10.3389/fneur.2022.812439

ISSN=1664-2295

ABSTRACT=Abstract
Objective: This study aims to detect the invisible metabolic abnormality in PET images of the patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using multivariate cross-classification method.
Methods: Participants were divided into training cohort and testing cohort. The training cohort included 17 healthy participants and 17 patients with anti-LGI1 encephalitis whose metabolic abnormality was able to be visibly detected in both the medial temporal lobe and the basal ganglia in their PET images (completely-detectable patients, CD patients). The testing cohort included another 16 healthy participants and 16 patients with anti-LGI1 encephalitis whose metabolic abnormality was not able to be visibly detected in the medial temporal lobe and the basal ganglia in their PET images (non-completely-detectable patients, non-CD patients). Independent component analysis (ICA) was used to extract features and reduce dimension. A logistic regression model was constructed to identify the non-CD patients.
Results: For the testing cohort, the accuracy of classification was 90.63% with 13 out of 16 non-CD patients identified and all healthy participants distinguished from non-CD patients. The patterns of PET signal changes resulting from metabolic abnormality related to anti-LGI1 encephalitis were similar for CD patients and non-CD patients.
Conclusion: The present study demonstrated that multivariate cross-classification combined with ICA could improve, to some degree, the detection of invisible abnormal metabolism in the PET images of patients with anti-LGI1 encephalitis. More importantly, the invisible metabolic abnormality in the PET images of non-CD patients presented actually similar patterns to that of CD patients.