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European Radiology

Erschienen in:

13.01.2022 | Nuclear Medicine

Individual [¹⁸F]FDG PET and functional MRI based on simultaneous PET/MRI may predict seizure recurrence after temporal lobe epilepsy surgery

verfasst von: Jingjuan Wang, Kun Guo, Bixiao Cui, Yaqin Hou, Guoguang Zhao, Jie Lu

Erschienen in: European Radiology | Ausgabe 6/2022

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Abstract

Objectives

To investigate the individual measures of brain glucose metabolism, neural activity obtained from simultaneous ¹⁸[F]FDG PET/MRI, and their association with surgical outcomes in medial temporal lobe epilepsy due to hippocampal sclerosis (mTLE-HS).

Methods

Thirty-nine unilateral mTLE-HS patients who underwent anterior temporal lobectomy were classified as having completely seizure-free (Engel class IA; n = 22) or non-seizure-free (Engel class IB–IV; n = 17) outcomes at 1 year after surgery. Preoperative [¹⁸F]FDG PET and functional MRI (fMRI) were obtained from a simultaneous PET/MRI scanner, and individual glucose metabolism and fractional amplitude of low-frequency fluctuation (fALFF) were evaluated by standardizing these with respect to healthy controls. These abnormality measures and clinical data from each patient were incorporated into a machine learning framework (gradient boosting decision tree and logistic regression analysis) to estimate seizure recurrence. The predictive values of features were evaluated by the receiver operating characteristic (ROC) curve in the training and test cohorts.

Results

The machine learning classification model showed [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network and age of onset identify unfavorable surgical outcomes effectively. In the validation dataset, the logistic regression model with [¹⁸F]FDG PET and fALFF obtained from simultaneous [¹⁸F]FDG PET/MRI gained the maximum area under the ROC curve of 0.905 for seizure recurrence, higher than 0.762 with ¹⁸[F]-FDG PET, and 0.810 with fALFF alone.

Conclusion

Machine learning model suggests individual [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network based on ¹⁸[F]-FDG PET/MRI could serve as a potential biomarker of unfavorable surgical outcomes.

Key Points

• Individual [ ¹⁸ F]FDG PET and fMRI obtained from preoperative [ ¹⁸ F]FDG PET/MR were investigated.

• Individual differences were further assessed based on a seizure propagation network.

• Machine learning can classify surgical outcomes with 90.5% accuracy.

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Titel: Individual [18F]FDG PET and functional MRI based on simultaneous PET/MRI may predict seizure recurrence after temporal lobe epilepsy surgery
verfasst von: Jingjuan Wang
Kun Guo
Bixiao Cui
Yaqin Hou
Guoguang Zhao
Jie Lu
Publikationsdatum: 13.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-08490-9

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Springer Medizin

Individual [¹⁸F]FDG PET and functional MRI based on simultaneous PET/MRI may predict seizure recurrence after temporal lobe epilepsy surgery

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Update Radiologie

Springer Medizin

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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