Hosung Kim1, Yee-Leng Sung Tan2, Tarik Tihan3, Anthony James Barkovich1, Duan Xu1, and Robert C Knowlton2
1Radiology & Biomedical Imaging, University california San francisco, San Francisco, CA, United States, 2Neurology, University california San francisco, SAN FRANCISCO, CA, United States, 3Pathology, University california San francisco, SAN FRANCISCO, CA, United States
Synopsis
Focal cortical dysplasia (FCD) is an epileptogenic
developmental malformation. Identification of this lesion can lead to a
successful surgery. We propose to analyze
a combined feature-set extracted from MRI and PET. Studying 29 FCD patients and 23 controls, classification using the combined MRI and PET features demonstrated
superior performance to the analysis of MRI as it resulted in a lower false
positive (FP) rate in controls (1.3% lower) and a higher sensitivity in FCD (7%
higher). Analysis of the combined MRI and PET revealed a larger FP rate in FCD compared
to MRI-only, suggesting the presence of extralesional pathology.Background and Purpose
Focal cortical
dysplasia (FCD) is an epileptogenic developmental malformation, characterized
by a range of abnormalities, including cortical dyslamination, cytomegalic
dysmorphic neurons, and balloon cells. FCD is a common cause of
intractable epilepsy in children, and the most common histopathology in
extratemporal lobe epilepsy
1. Identification of this lesion can
lead to a successful epilepsy surgery
1. Because the scalp
electrochephalogram has a low spatial resolution, anatomical MRIs such as t1-
and t2-weighted images have been widely used to improve detection of
epileptogenic lesions to aid localization of focal epilepsy. Combination of a pattern
classification with quantitative imaging features representing FCD lesions
including cortical thickening, blurring of gray matter (GM) – white matter (WM)
junction, and hyperintense GM signal, has dramatically enhanced the performance
of the FCD identification
2. A further improvement of the
sensitivity, however, is required for clinical practice
2. Recent
evidence has suggested positron emission tomography (PET) may complement the
sensitivity of MRI, in particular for cases with small or more subtle lesions,
including those that are cryptogenic, i.e. “MRI-negative”
3. We
propose a framework to analyze a set of features extracted from both MRI and
PET to address the need for improved diagnostic sensitivity. We used patients
with temporal lobe epilepsy as controls because it is difficult to recruit
healthy controls in most clinical environments.
Methods
We studied 29 FCD patients
and 23 TLE controls (Table 1). No FCD patients presented neurological abnormalities
other than FCD or dual pathology (e.g., mesiotemporal sclerosis). Individuals underwent
T1-weighted MRI scanning and FDG-PET scanning. The MR images were first processed
to correct intensity non-uniformity and to normalize the intensity range. They
were then registered to the MNI-ICBM template. Cortical surfaces representing
the WM-GM and GM-CSF interface were extracted from the registered MRI
4.
The PET image and MR image were co-registered within an individual using a mutual
information-based rigid-body transformation (Fig1A). To avoid data interpolation related to registration,
features were computed after surfaces were transformed back into each image’s
native space. The following features were computed using cortical surfaces: cortical thickness, sulcal depth (small lesions are known to be located in the bottom of a deep sulcus), relative intensity (hyperintensity in GM), gradient
along surface-normal direction (i.e., blurring), and PET
intensity (normalized using total WM intensity and CSF intensity in the lateral
ventricle). Each feature was normalized: (1) across all the surface
points within a given individual (intra-subject normalization); and (2) feature
values at a given point were normalized with respect to the distribution of TLE
controls (inter-subject normalization). Classification was performed using
Fisher linear discriminant analysis that found the optimal weights for a linear combination of features to achieve
maximal separation between classes. We trained and cross-validated the
classifier using a leave-one-out strategy, by which a patient is classified
based on data of all patients other than that patient.
Results
An example of
classification is shown in Fig1B. For
both analyses of MRI only and combined MRI and PET features, we found the best
trade-off between the sensitivity and specificity at the posterior probability
of 91% in the classifier as we obtained 100% detection rate of true positive
(TP) FCD lesions (29/29) and the lowest false positive (FP) rates (Table 2). Classification using the combined
MRI and PET features demonstrated superior performance to the analysis of MRI
as it resulted in a lower FP rate in TLE controls (1.3% lower). On the other
hand, use of the combined MRI and PET revealed a larger FP rate in FCD patients
compared to using MRI only. This pattern was kept even at the highest posterior
probability (99%) where very low FP rate was found in TLE controls (0.6%),
suggesting the presence of extralesional pathology. At the maximum posterior
probability, the combined analysis of MRI and PET also showed higher
sensitivity to FCD lesions than the MRI only (86% vs. 79%).
Conclusions
Our analysis of the
clinical utility of imaging features for the detection of FCD lesions showed
that superior sensitivity and specificity of the classification were obtained
by combining PET with MRI, when compared to the conventional analysis of MRI
only. Our analysis further demonstrated the presence of extra-lesional brain
abnormalities in patients with FCD, which is supported by a previous report
2.
Use of TLE controls in our study yielded classification performance parallel to
using healthy controls as reported in a previous analysis
2,
suggesting the possibility to exploit more widely available databases. An
additional classification step may be required to remove the relatively large
amount of FP clusters in FCD patients
3,5.
Acknowledgements
No acknowledgement found.References
1. Blümcke I et al.,
Epilepsia 2011; 2. Hong SJ et al., Neurology 2014; 3. Guerrini R et al.,
Epilepsia 2015; 4. Kim JS et al., Neuroimage 2005; 5. Besson P et al., MICCAI
2008.