Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy

Author:

Flaus Anthime1234ORCID,Jung Julien45ORCID,Ostrowky‐Coste Karine46,Rheims Sylvain45ORCID,Guénot Marc47,Bouvard Sandrine4,Janier Marc12,Yaakub Siti N.8,Lartizien Carole9ORCID,Costes Nicolas410,Hammers Alexander3

Affiliation:

1. Department of Nuclear Medicine Hospices Civils de Lyon Lyon France

2. Medical Faculty of Lyon Est University Claude Bernard Lyon 1 Lyon France

3. King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences King's College London London UK

4. Lyon Neuroscience Research Center INSERM U1028/CNRS UMR5292 Lyon France

5. Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Member of the ERN EpiCARE Lyon 1 University Lyon France

6. Department of Pediatric Clinical Epileptology, Sleep Disorders, and Functional Neurology Hospices Civils de Lyon, Member of the ERN EpiCARE Lyon France

7. Department of Functional Neurosurgery, Hospices Civils de Lyon, Member of the ERN EpiCARE Lyon 1 University Lyon France

8. Brain Research & Imaging Centre University of Plymouth Plymouth UK

9. INSA‐Lyon, CNRS, Inserm, CREATIS UMR 5220, U1294 University Claude Bernard Lyon 1 Lyon France

10. CERMEP‐Life Imaging Lyon France

Abstract

AbstractObjectiveNormal interictal [18F]FDG‐PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co‐registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug‐resistant epilepsy (DRE) with reference to visual and statistical parametric mapping (SPM) analysis.MethodsPatients with complete presurgical work‐up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed.ResultsTwenty patients aged 17‐50 (32 ± 7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI‐negative. After surgery, 14 patients (70%) had a good outcome (Engel I‐II). RZ localisation rate was 60% with SIPCOM vs 35% using SPM (P = 0.015) and vs 85% using visual analysis (P = 0.54). Results were similar for Engel I‐II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI‐positive vs 50% in MRI‐negative patients, and 64% in TLE vs 43% in extra‐TLE. The average number of false‐positive clusters was 2.2 ± 1.3 using SIPCOM vs 2.3 ± 3.1 using SPM. All RZs localized with SPM were correctly localized with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localized the RZ.SignificanceSIPCOM performed better than the reference computer‐assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated.

Funder

LabEx PRIMES

Wellcome Trust

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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