Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

Yen Cheng Shih, Tse Hao Lee, Hsiang Yu Yu, Chien Chen Chou, Cheng Chia Lee, Po Tso Lin, Syu Jyun Peng

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose 18F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of 18F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. Patients and Methods We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. Result Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. Conclusions Visual analysis of 18F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing 18F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.

Original languageEnglish
Pages (from-to)287-293
Number of pages7
JournalClinical Nuclear Medicine
Volume47
Issue number4
DOIs
Publication statusPublished - Apr 1 2022

Keywords

  • F-FDG PET
  • machine learning
  • medial temporal lobe epilepsy
  • quantitative PET

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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