| Literature DB >> 26386662 |
Peng Fu1, Fang Zhang1, Jianqing Gao1, Jianmin Jing1, Liping Pan1, Dongxue Li1, Lingge Wei1.
Abstract
BACKGROUND The aim of this study was to explore the value of NeuroGam software in diagnosis of epilepsy by 99Tcm-ethyl cysteinate dimer (ECD) brain imaging. MATERIAL AND METHODS NeuroGam was used to analyze 52 cases of clinically proven epilepsy by 99Tcm-ECD brain imaging. The results were compared with EEG and MRI, and the positive rates and localization to epileptic foci were analyzed. RESULTS NeuroGam analysis showed that 42 of 52 epilepsy cases were abnormal. 99Tcm-ECD brain imaging revealed a positive rate of 80.8% (42/52), with 36 out of 42 patients (85.7%) clearly showing an abnormal area. Both were higher than that of brain perfusion SPECT, with a consistency of 64.5% (34/52) using these 2 methods. Decreased regional cerebral blood flow (rCBF) was observed in frontal (18), temporal (20), and parietal lobes (2). Decreased rCBF was seen in frontal and temporal lobes in 4 out of 36 patients, and in temporal and parietal lobes of 2 out of 36 patients. NeuroGam further showed that the abnormal area was located in a different functional area of the brain. EEG abnormalities were detected in 29 out of 52 patients (55.8%) with 16 cases (55.2%) clearly showing an abnormal area. MRI abnormalities were detected in 17 out of 43 cases (39.5%), including 9 cases (52.9%) clearly showing an abnormal area. The consistency of NeuroGam software analysis, and EEG and MRI were 48.1% (25/52) and 34.9% (15/43), respectively. CONCLUSIONS NeuroGam software analysis offers a higher sensitivity in detecting epilepsy than EEG or MRI. It is a powerful tool in 99Tcm-ECD brain imaging.Entities:
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Year: 2015 PMID: 26386662 PMCID: PMC4581683 DOI: 10.12659/MSM.894566
Source DB: PubMed Journal: Med Sci Monit ISSN: 1234-1010
Participants’ clinical demographics.
| Epilepsy patients | Control group | ||
|---|---|---|---|
| Number of cases | 52 | 12 | |
| Age years | 37.2±13.4 (13~58) | 32.4±11.7 (19~52) | |
| Sex (M/F) | 25/27 | 7/5 | |
| Seizure types (n) | GTCS (15) | ||
| Partial seizure (37) | Simple partial seizure (8) | ||
| Complex partial seizure (12) | |||
| GTCS secondary to partial seizure (17) | |||
GTCS – generalized tonic-clonic seizure.
The Z value of decreased rCBF in the cerebral lobes of epilepsy patients and controls.
| Frontal lobe | Temporal lobe | Parietal lobe | Occipital lobe | Insular lobe | |
|---|---|---|---|---|---|
| Control group (n=12) | −1.1±0.3 | −1.0±0.6 | −1.2±0.7 | −1.0±0.4 | −0.9±0.7 |
| Epilepsy group (n=34) | −3.2±0.9 | −3.4±1.0 | −3.3±0.8 | −0.8±0.6 | −1.0±0.2 |
Consistency of results with NeuroGam software analysis and EEG and MRI.
| Number of cases | Exact consistency | Partial consistency | Complete inconsistency | |
|---|---|---|---|---|
| NeuroGam software analysis and EEG | 52 | 20 (38.5%) | 5 (9.6%) | 27 (51.9%) |
| NeuroGam software analysis and MRI | 43 | 11 (25.6%) | 4 (9.3%) | 28 (65.1%) |
Figure 1Typical images of perfusion defects in a 43-year-old female patient with epilepsy. NeuroGam software analysis revealed areas of decreased rCBF in the left frontal lobe (Br 44) and bilateral temporal lobe (Br 38).
Figure 2Typical images of respective perfusion defects in a 43-year-old female patient with epilepsy are shown. Semi-quantitative analysis of the decreased areas of rCBF, which was compared with the regional database by z-score, showed a decrease of 2.7 standard deviations in the left frontal lobe (Br 44) and a decrease of 1.9 and 1.3 standard deviations in bilateral temporal lobe (Br 38).