Automated MRI Volumetric Analysis in Patients with Rasmussen Syndrome

Abstract

Background and purpose: Rasmussen syndrome, also known as Rasmussen encephalitis, is typically associated with volume loss of the affected hemisphere of the brain. Our aim was to apply automated quantitative volumetric MR imaging analyses to patients diagnosed with Rasmussen encephalitis, to determine the predictive value of lobar volumetric measures and to assess regional atrophy differences as well as monitor disease progression by using these measures.

Materials and methods: Nineteen patients (42 scans) with diagnosed Rasmussen encephalitis were studied. We used 2 control groups: one with 42 age- and sex-matched healthy subjects and the other with 42 epileptic patients without Rasmussen encephalitis with the same disease duration as patients with Rasmussen encephalitis. Volumetric analysis was performed on T1-weighted images by using BrainSuite. Ratios of volumes from the affected hemisphere divided by those from the unaffected hemisphere were used as input to a logistic regression classifier, which was trained to discriminate patients from controls. Using the classifier, we compared the predictive accuracy of all the volumetric measures. These ratios were used to further assess regional atrophy differences and correlate with epilepsy duration.

Results: Interhemispheric and frontal lobe ratios had the best prediction accuracy for separating patients with Rasmussen encephalitis from healthy controls and patient controls without Rasmussen encephalitis. The insula showed significantly more atrophy compared with all the other cortical regions. Patients with longitudinal scans showed progressive volume loss in the affected hemisphere. Atrophy of the frontal lobe and insula correlated significantly with epilepsy duration.

Conclusions: Automated quantitative volumetric analysis provides accurate separation of patients with Rasmussen encephalitis from healthy controls and epileptic patients without Rasmussen encephalitis, and thus may assist the diagnosis of Rasmussen encephalitis. Volumetric analysis could also be included as part of follow-up for patients with Rasmussen encephalitis to assess disease progression.

Fig 1.

Illustration of 2 serial MRIs from the same patient (P6) and the SVReg output of BrainSuite. First row: MR imaging at 10 years of age. Second row: MR imaging at 17 years of age. Shown in the left column are the sagittal T1-weighted MPRAGE images. The right column is the cortical rendering of the SVReg labels, with different colors denoting different anatomic areas of the brain. Pronounced atrophy can be observed at the left peri-Sylvian area, and HRvol shows a decrease from 0.79 to 0.70.

Fig 2.

Absolute volume of the right hemisphere versus volume of the left hemisphere in patients with RE and 2 control groups. Triangles indicate healthy controls; crosses, controls with non-RE epilepsy; squares, patients with left RE; and circles, patients with right RE. The dashed line is the diagonal line representing hemispheres with equal volumes.

Fig 3.

Mean accuracy across the 5 cross-validation runs of the logistic regression classifier for 15 volumetric ratio measures. Patients with RE were compared with controls with non-RE epilepsy with matching disease durations. Error bars denote the SD. The mean accuracy values for each measure were plotted at the bottom of the bars. INS indicates insula; F, frontal; T, temporal; P, parietal; O, occipital; AH, amygdala and hippocampus; PU, putamen; CAU, caudate nucleus; TH, thalamus; GP, globus pallidus; BST, brain stem; and CERE, cerebellum.

Fig 4.

Performance of the classifier using HRvol. Patients with RE are denoted with dots, and controls with non-RE epilepsy were denoted with crosses. True-positives (TP) are defined as patients correctly identified as patients by the classifier. True-negatives (TN) are defined as controls correctly identified as controls. False-positives (FP) are defined as controls incorrectly identified as patients. False-negatives (FN) are defined as patients incorrectly identified as controls. Circled dots/crosses denote the subjects who were misclassified (3 FPs and 3 FNs).

Fig 5.

Receiver operating characteristic analyses showing a highly discriminative classifier using HRvol.

Fig 6.

Probability curves depicting the relationship between HRvol and the probability of RE. The solid curve was estimated on the basis of comparison of 42 scans from patients with RE and 42 scans from controls with non-RE epilepsy with the same disease duration. The thin dashed curve was additionally generated to correct for the difference in incidence of RE and non-RE epilepsy (1 in 1,000,000 versus 1 in 100).

Fig 7.

A, HRvol plotted over epilepsy duration in the 9 patients with serial MR imaging. All except P5 show a decrease in HRvol for the observed time. The axis is broken from 14 to 20 years because there are no data points for these durations. B, Absolute hemispheric volume (right-sided versus left-sided plots) of the same 9 patients. The direction of each dotted arrow shows the progression of disease over time in each patient. A and B share the same symbol for each patient for direct comparison.