Evidence map›Paper›PMID 34475125›Full record

ArticleNeurology2021

MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy.

Benoit Caldairou, Niels A Foit, Carlotta Mutti, Fatemeh Fadaie, Ravnoor Gill, Hyo Min Lee, Theo Demerath, Horst Urbach, Andreas Schulze-Bonhage, Andrea Bernasconi and 1 more

Erratum issuedOpen access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

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  19. Prediction of neuropathologic lesions from clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 1 institution in 2 countries.

Benoit CaldairouFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Niels A FoitFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Carlotta MuttiFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Fatemeh FadaieFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Ravnoor GillFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Hyo Min LeeFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Theo DemerathFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Horst UrbachFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Andreas Schulze-BonhageFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
Andrea BernasconiFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany. neda@bic.mni.mcgill.ca.
Neda BernasconiFrom the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany. neda@bic.mni.mcgill.ca.
Montreal Neurological Institute and Hospital · CA

Funding

CIHR MOP-123520CIHR MOP-57840
6 · The paper itself

Abstract

BACKGROUND AND

objectivesMRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE.

methodsWe trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative).

resultsThe overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 ± 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. DISCUSSION: This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.

Indexed as

Machine LearningAdolescentAdultEpilepsy, Temporal LobeFemaleFunctional LateralityHippocampusHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingSclerosisYoung Adult

Identifiers

PMID34475125
PMCPMC8548960
OpenAlexW3196821541

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.