Predicting the Outcome of Epilepsy Surgery by Covariance Pattern Analysis of Ictal Perfusion SPECT

• Published in: Journal of Nuclear Medicine Vol. 63; no. 6; pp. 925 - 930
• Main Authors: Taherpour, Jila, Jaber, Mariam, Voges, Berthold, Apostolova, Ivayla, Sauvigny, Thomas, House, Patrick M, Lanz, Michael, Lindenau, Matthias, Klutmann, Susanne, Martens, Tobias, Stodieck, Stefan, Buchert, Ralph
• Format: Journal Article
• Language: English
• Published: United States Society of Nuclear Medicine 01-06-2022
• Subjects: Brain; Convulsions & seizures; Covariance; Demographic variables; Epilepsy; Epilepsy, Temporal Lobe - diagnostic imaging; Epilepsy, Temporal Lobe - surgery; Humans; Magnetic Resonance Imaging; Medical imaging; Patients; Pattern analysis; Perfusion; Qualitative analysis; Retrospective Studies; Seizures; Seizures - diagnostic imaging; Seizures - surgery; Single photon emission computed tomography; Surgery; Temporal lobe; Tomography, Emission-Computed, Single-Photon; epilepsy; covariance analysis; ictal; SPECT; surgery
• ISSN: 0161-5505; 1535-5667; 2159-662X
• DOI: 10.2967/jnumed.121.262702

Previous studies on the utility of specific perfusion patterns in ictal brain perfusion SPECT for predicting the outcome of temporal lobe epilepsy surgery used qualitative visual pattern classification, semiquantitative region-of-interest analysis, or conventional univariate voxel-based testing, which are limited by intra- and interrater variability or low sensitivity to capture functional interactions among brain regions. The present study performed covariance pattern analysis of ictal perfusion SPECT using the scaled subprofile model for unbiased identification of predictive covariance patterns.
The study retrospectively included 18 responders to temporal lobe epilepsy surgery (Engel I-A at 12 mo follow-up) and 18 nonresponders (≥Engel I-B). Ictal SPECT images were analyzed with the scaled subprofile model masked to group membership for unbiased identification of the 16 covariance patterns explaining the highest proportion of variance in the whole dataset. Individual expression scores of the covariance patterns were evaluated for predicting seizure freedom after temporal lobe surgery by receiver-operating-characteristic analysis. Kaplan-Meier analysis including all available follow-up data (up to 60 mo after surgery) was also performed.
Among the 16 covariance patterns only 1 showed a different expression between responders and nonresponders (
= 0.03). This favorable ictal perfusion pattern resembled the typical ictal perfusion pattern in temporomesial epilepsy. The expression score of the pattern provided an area of 0.744 (95% CI, 0.577-0.911,
= 0.004) under the receiver-operating-characteristic curve. Kaplan-Meier analysis revealed a statistical trend toward longer seizure freedom in patients with positive expression score (
= 0.06). The median estimated seizure-free time was 48 mo in patients with positive expression score versus 6 mo in patients with negative expression score.
The expression of the favorable ictal perfusion pattern identified by covariance analysis of ictal brain perfusion SPECT provides independent (from demographic and clinical variables) information for the prediction of seizure freedom after temporal lobe epilepsy surgery. The expression of this pattern is easily computed for new ictal SPECT images and, therefore, might be used to support the decision for or against temporal lobe surgery in clinical patient care.