DOI: 10.5061/dryad.h70rxwdgm
title: Supplement: Multicenter validated detection of focal cortical dysplasia using deep learning

description: Objective. To test the hypothesis that a multicenter-validated computer
 deep learning algorithm detects MRI-negative focal cortical dysplasia
 (FCD). Methods. We used clinically acquired 3D T1-weighted and 3D FLAIR
 MRI of 148 patients (median age, 23 years [range, 2-55]; 47% female) with
 histologically verified FCD at nine centers to train a deep convolutional
 neural network (CNN) classifier. Images were initially deemed as
 MRI-negative in 51% of cases, in whom intracranial EEG determined the
 focus. For risk stratification, the CNN incorporated Bayesian uncertainty
 estimation as a measure of confidence. To evaluate performance, detection
 maps were compared to expert FCD manual labels. We also tested sensitivity
 in an independent cohort of 23 FCD cases (13±10 years). Applying the
 algorithm to 38 healthy and 63 temporal lobe epilepsy disease controls
 tested specificity. Results. Overall sensitivity was 93% (137/148 FCD
 detected) using a leave-one-site-out cross-validation, with an average of
 six false positives per patient. Sensitivity in MRI-negative FCD was 85%.
 In 73% of patients, the FCD was among the clusters with the highest
 confidence; in half it ranked the highest. Sensitivity in the independent
 cohort was 83% (19/23; average of five false positives per patient).
 Specificity was 89% in healthy and disease controls. Conclusions. This
 first multicenter-validated deep learning detection algorithm yields the
 highest sensitivity to date in MRI-negative FCD. By pairing predictions
 with risk stratification this classifier may assist clinicians to adjust
 hypotheses relative to other tests, increasing diagnostic confidence.
 Moreover, generalizability across age and MRI hardware makes this approach
 ideal for pre-surgical evaluation of MRI-negative epilepsy. Classification
 of evidence. This study provides Class III evidence that deep learning on
 multimodal MRI accurately identifies FCD in epilepsy patients initially
 diagnosed as MRI-negative.
description: 1. Description of methods used for collection/generation of data
 [noel_deepFCD_patch_16x16x16.h5]:<br> To create
 the HDF5 dataset, for each of the 148 FCD patients, we sampled at most
 1,000 cortical patches (or # voxels in the lesion, whichever is lower) of
 size 16×16×16 within the lesion on pre-processed T1- and T2-weighted FLAIR
 MRI. The same number of cortical patches were sampled randomly outside the
 lesion. The resulting lesional and non-lesional patches were concatenated,
 shuffled (to add another layer of randomization), and saved along with
 their binary labels (as a compressed HDF5 dataset). Refer original
 publication and
 FCD_Detection_Neurology_Supplement.docx for more
 details 2. Methods for processing the data
 [noel_deepFCD_patch_16x16x16.h5]:<br> MRI
 pre-processing involved linear registration to the MNI152 symmetric
 template, non-uniformity correction, intensity standardization with
 scaling of values between 0 and 100, and skull-stripping using an in-house
 deep learning method (doi.org/10.5281/zenodo.4521716). Refer original publication and FCD_Detection_Neurology_Supplement.docx for details 3. Instrument- or software-specific information needed to interpret the data [noel_deepFCD_patch_16x16x16.h5]:<br> Python 3.5+ with h5py package (version &gt;= 2.9.0)
description: FCD_Detection_Neurology_Supplement.docx
 contains: <b>Table e-1.</b> MRI
 acquisition parameters across sites <b>Figure e-1. </b>Hierarchical
 patch-based feature learning using CNN 
 <b>Additional
 Methods:</b> Classifier design 
 Source code and data
 availability 
 <b>Table
 e-2.</b> Peak location of FCD lesions in
 MNI space <b>Table
 e-3.</b> Peak location of false positive
 clusters in MNI space <strong>eReferences</strong> noel_deepFCD_patch_16x16x16.h5 (size: 6.4GB) contains two variables (data and labels): also available from: https://doi.org/10.5281/zenodo.3239446 variables array shape description data {282736, 2, 16, 16, 16} The data variable contains a numpy array (numpy.float) with 2,82,736 multimodal (T1- and T2-weighted FLAIR) patches of size 16×16×16. See S<em>ource code and data availability</em> section in Additional Methods (FCD_Detection_Neurology_Supplement.docx) for details labels {282736} Corresponding set of binary labels (numpy.int) for each of the 2,82,736 patches. 1 indicates lesional, 0 indicates nonlesional This data can be read in Python (using h5py =&gt; 2.9.0) using the included read_h5data.py function. FCD_Detection_Neurology_STARD-Checklist.docx contains: STARD 2015 Checklist: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. Please refer to https://www.equator-network.org/reporting-guidelines/stard/ for details. only available from: https://doi.org/10.5281/zenodo.5173104