Angela Lignelli

SPECT imaging of odor identification in schizophrenia

Deficits in olfactory identification, despite normal odor perception, are found in some neuropsychiatric disorders, including schizophrenia. We examined if regional cerebral blood flow (rCBF) differed between schizophrenia patients and controls during odor identification, hypothesizing that these brain regions could be relevant to odor identification impairments. Eight schizophrenia and eight comparison subjects provided a baseline (picture identity matching) and activation (odor identification) SPECT scan, obtained using 99mTc-HMPAO in a low dose/high dose design. Six patients and seven controls had analyzable data. MEDX data saved in ANALYZE format for SPM 95 generated paired t-test statistical data for display in Talairach space, with rCBF changes given as Z-scores. There was no schizophrenia vs. control group difference in rCBF for the baseline picture-matching test. For odor identification, schizophrenia patients had a hypometabolic right-sided cortical region that included the frontal lobe Brocas area, superior temporal lobe, and supramarginal and angular gyri. Post hoc within-group contrasts of picture-matching vs. odor identification showed that the controls significantly increased rCBF in the right-sided inferior temporal fusiform gyrus, and bilateral hippocampi and visual association areas for the odor test. The schizophrenia group showed no rCBF differences for picture-matching compared to odor identification. Patients showed significant hypometabolism in right-sided cortical areas for odor identification. They also failed to show increased rCBF in the hippocampus and visual association area, as seen in controls for odor identification compared to picture-matching. These regions may be unique to schizophrenia or have broader implications for olfactory memory retrieval.

Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.

BACKGROUND AND PURPOSE: A limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor. MATERIALS AND METHODS: This retrospective study included patients with native glioblastomas with MR imaging performed at 24–48 hours following resection and 2–4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques. RESULTS: Twenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990–0.997) compared with 1D (0.826; 95% CI, 0.695–0.904) and 2D (0.905; 95% CI, 0.828–0.948) measurements. CONCLUSIONS: Computer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.

Review of Imaging Techniques in the Diagnosis and Management of Brain Metastases

Brain metastasis is one of the most common diagnoses encountered by neurologists, neurosurgeons, radiologists, and oncologists. The aim of this article is to review imaging modalities used in the diagnosis and follow-up of brain metastases. Through the use of various imaging techniques more accurate preoperative diagnosis and more precise intraoperative planning can be made. Post-treatment evaluation can also be refined through the use of these imaging techniques.

Glioblastoma Induces Vascular Dysregulation in Nonenhancing Peritumoral Regions in Humans

OBJECTIVE Glioblastoma is an invasive primary brain malignancy that typically infiltrates the surrounding tissue with malignant cells. It disrupts cerebral blood flow through a variety of biomechanical and biochemical mechanisms. Thus, neuroimaging focused on identifying regions of vascular dysregulation may reveal a marker of tumor spread. The purpose of this study was to use blood oxygenation level-dependent (BOLD) functional MRI (fMRI) to compare the temporal dynamics of the enhancing portion of a tumor with those of brain regions without apparent tumors. MATERIALS AND METHODS Patients with pathologically proven glioblastoma underwent preoperative resting-state BOLD fMRI, T1-weighted contrast-enhanced MRI, and FLAIR MRI. The contralesional control hemisphere, contrast-enhancing tumor, and peritu-moral edema were segmented by use of structural images and were used to extract the time series of these respective regions. The parameter estimates (beta values) for the two regressors and resulting z-statistic images were used as a metric to compare the similarity of the tumor dynamics to those of other brain regions. RESULTS The time course of the contrast-enhancing tumor was significantly different from that of the rest of the brain (p < 0.05). Similarly, the control signal intensity was significantly different from the tumor signal intensity (p < 0.05). Notably, the temporal dynamics in the peritumoral edema, which did not contain enhancing tumor, were most similar to the those of enhancing tumor than to those of control regions. CONCLUSION The findings show that the disruption in vascular regulation induced by a glioblastoma can be detected with BOLD fMRI and that the spatial distribution of these disruptions is localized to the immediate vicinity of the tumor and peritumoral edema.

Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images.

OBJECTIVE Recurrence of glioblastoma multiforme (GBM) arises from areas of microscopic tumor infiltration that have yet to disrupt the blood-brain barrier. We hypothesize that these microscopic foci of invasion cause subtle variations in the apparent diffusion coefficient (ADC) and FLAIR signal detectable with the use of computational big-data modeling. MATERIALS AND METHODS Twenty-six patients with native GBM were studied immediately after undergoing gross total tumor resection. Within the peritumoral region, areas of future GBM recurrence were identified through coregistration of follow-up MRI examinations. The likelihood of tumor recurrence at each individual voxel was assessed as a function of signal intensity on ADC maps and FLAIR images. Both single and combined multivariable logistic regression models were created. RESULTS A total of 419,473 voxels of data (105,477 voxels of data within tumor recurrence and 313,996 voxels of data on surrounding peritumoral edema) were analyzed. For future areas of recurrence, a 9.5% decrease in the ADC value (p < 0.001) and a 9.2% decrease in signal intensity on FLAIR images (p < 0.001) were shown, compared with findings for the surrounding peritumoral edema. Logistic regression revealed that the amount of signal loss on both ADC maps and FLAIR images correlated with the likelihood of tumor recurrence. A combined multiparametric logistic regression model was more specific in the prediction of tumor recurrence than was either single-variable model alone. CONCLUSION Areas of future GBM recurrence exhibit small but highly statistically significant differences in signal intensity on ADC maps and FLAIR images months before the development of abnormal enhancement occurs. A multiparametric logistic model calibrated to these changes can be used to estimate the burden of microscopic nonenhancing tumor and predict the location of recurrent disease. Computational big-data modeling performed at the voxel level is a powerful technique capable of discovering important but subtle patterns in imaging data.

A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies

Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. T2-FLAIR and ADC sequences were inversely correlated with cell density. T1-postcontrast subtraction was directly correlated with cell density. The authors conclude that the model illustrates a quantitative and significant relationship between MR signal and cell density. Applying this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity for both enhancing core and nonenhancing margins. BACKGROUND AND PURPOSE: The complex MR imaging appearance of glioblastoma is a function of underlying histopathologic heterogeneity. A better understanding of these correlations, particularly the influence of infiltrating glioma cells and vasogenic edema on T2 and diffusivity signal in nonenhancing areas, has important implications in the management of these patients. With localized biopsies, the objective of this study was to generate a model capable of predicting cellularity at each voxel within an entire tumor volume as a function of signal intensity, thus providing a means of quantifying tumor infiltration into surrounding brain tissue. MATERIALS AND METHODS: Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. Signal intensity was plotted against cell density for each MR image. RESULTS: T2-FLAIR (r = −0.61) and ADC (r = −0.63) sequences were inversely correlated with cell density. T1-postcontrast (r = 0.69) subtraction was directly correlated with cell density. Combining these relationships yielded a multiparametric model with improved correlation (r = 0.74), suggesting that each sequence offers different and complementary information. CONCLUSIONS: Using localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.

Optic nerve glioma: case series with review of clinical, radiologic, molecular, and histopathologic characteristics.

Purpose: This study was designed to better understand the biologic nature of optic nerve gliomas (ONGs) and to investigate staining techniques that might improve the pathologic interpretation of surgical margins. Methods: In this retrospective case series, clinical data on patient presentation, MRI, surgical visualization, and initial pathologic interpretation were gathered. Specimens were then reexamined using analysis of p53, isocitrate dehydrogenase 1 (IDH1), MIB-1, and B-rapidly accelerated fibrosarcoma (BRAF) duplication. Results: Six patients were studied. All were diagnosed with World Health Organization grade 1 ONGs on original pathology. On reexamination, BRAF tandem duplication was found in 2 patients with neurofibromatosis Type 1 association. P53 immunoreactivity was noted in a third case. No cases had IDH1 immunoreactivity. Focal elevations of MIB-1 up to 7.5% were noted in 2 cases. Conclusions: ONGs are neoplasms with variable degrees of aggressiveness. As more is understood regarding their varied genetic underpinnings, improved pathologic classification and individualized treatment regimens may be achieved. The authors hope that this study helps guide the oculoplastic community toward a multi-institutional, prospective study of ONG genomic sequencing.

Extent of BOLD Vascular Dysregulation Is Greater in Diffuse Gliomas without Isocitrate Dehydrogenase 1 R132H Mutation

Purpose To determine the effect that R132H mutation status of diffuse glioma has on extent of vascular dysregulation and extent of residual blood oxygen level-dependent (BOLD) abnormality after surgical resection. Materials and Methods This study was an institutional review board-approved retrospective analysis of an institutional database of patients, and informed consent was waived. From 2010 to 2017, 39 treatment-naïve patients with diffuse glioma underwent preoperative echo-planar imaging and BOLD functional magnetic resonance imaging. BOLD vascular dysregulation maps were made by identifying voxels with time series similar to tumor and dissimilar to healthy brain. The spatial overlap between tumor and vascular dysregulation was characterized by using the Dice coefficient, and areas of BOLD abnormality outside the tumor margins were quantified as BOLD-only fraction (BOF). Linear regression was used to assess effects of R132H status on the Dice coefficient, BOF, and residual BOLD abnormality after surgical resection. Results When compared with R132H wild-type (R132H-) gliomas, R132H-mutated (R132H+) gliomas showed greater spatial overlap between BOLD abnormality and tumor (mean Dice coefficient, 0.659 ± 0.02 [standard error] for R132H+ and 0.327 ± 0.04 for R132H-; P < .001), less BOLD abnormality beyond the tumor margin (mean BOF, 0.255 ± 0.03 for R132H+ and 0.728 ± 0.04 for R132H-; P < .001), and less postoperative BOLD abnormality (residual fraction, 0.046 ± 0.0047 for R132H+ and 0.397 ± 0.045 for R132H-; P < .001). Receiver operating characteristic curve analysis showed high sensitivity and specificity in the discrimination of R132H+ tumors from R132H- tumors with calculation of both Dice coefficient and BOF (area under the receiver operating characteristic curve, 0.967 and 0.977, respectively). Conclusion R132H mutation status is an important variable affecting the extent of tumor-associated vascular dysregulation and the residual vascular dysregulation after surgical resection.