Risk of early failure of VP shunts implanted for hydrocephalus after craniotomies for brain tumors in adults.

Risks and survival times of ventriculoperitoneal (VP) shunts implanted due to hydrocephalus after craniotomies for brain tumors are largely unknown. The purpose of this study was to determine the overall timing of VP shunting and its failure after craniotomy for brain tumors in adults. The authors also wished to explore risk factors for early VP shunt failure (within 90 days). A population-based consecutive patient cohort of all craniotomies for intracranial tumors leading to VP shunt dependency in adults (> 18 years) from 2004 to 2013 was studied. Patients with pre-existing VP shunts prior to craniotomy were excluded. The survival time of VP shunts, i.e., the shunt longevity, was calculated from the day of shunt insertion post-craniotomy for a brain tumor until the day of shunt revision requiring replacement or removal of the shunt system. Out of 4774 craniotomies, 85 patients became VP shunt-dependent (1.8% of craniotomies). Median time from craniotomy to VP shunting was 1.9 months. Patients with hydrocephalus prior to tumor resection (N = 39) had significantly shorter time to shunt insertion than those without (N = 46) (p < 0.001), but there was no significant difference with respect to early shunt failure. Median time from shunt insertion to shunt failure was 20 days (range 1-35). At 90 days, 17 patients (20%) had confirmed shunt failure. Patient age, sex, tumor location, primary/secondary craniotomy, extra-axial/intra-axial tumor, ventricular entry, post-craniotomy bleeding, and infection did not show statistical significance. The risk of early shunt failure (within 90 days) of shunts after craniotomies for brain tumors was 20%. This study can serve as benchmark for future studies.

The effect of tumor removal via craniotomies on preoperative hydrocephalus in adult patients with intracranial tumors.

The efficacy of tumor removal via craniotomies on preoperative hydrocephalus (HC) in adult patients with intracranial tumors is largely unknown. Therefore, we sought to evaluate the effect of tumor resection in patients with preoperative HC and identify the incidence and risk factors for postoperative VP shunt dependency. All craniotomies for intracranial tumors at Oslo University Hospital in patients ≥ 18 years old during a 10-year period (2004-2013) were reviewed. Patients with radiologically confirmed HC requiring surgery and subsequent development of shunt dependency were identified by cross-linking our prospectively collected tumor database to surgical procedure codes for hydrocephalus treatment (AAF). Patients with preexisting ventriculoperitoneal (VP) shunts (N = 41) were excluded. From 4774 craniotomies performed on 4204 patients, a total of 373 patients (7.8%) with HC preoperatively were identified. Median age was 54.4 years (range 18.1-83.9 years). None were lost to follow-up. Of these, 10.5% (39/373) required permanent CSF shunting due to persisting postoperative HC. The risk of becoming VP shunt dependent in patients with preexisting HC was 7.0% (26/373) within 30 days and 8.9% (33/373) within 90 days. Only secondary (repeat) surgery was a significant risk factor for VP shunt dependency. In this large, contemporary, single-institution consecutive series, 10.5% of intracranial tumor patients with preoperative HC became shunt-dependent post-craniotomy, yielding a surgical cure rate for HC of 89.5%. To the best of our knowledge, this is the first and largest study regarding postoperative shunt dependency after craniotomies for intracranial tumors, and can serve as benchmark for future studies.

The Impact of MRI Features and Observer Confidence on the Treatment Decision-Making for Patients with Untreated Glioma.

In a blind, dual-center, multi-observer setting, we here identify the pre-treatment radiologic features by Magnetic Resonance Imaging (MRI) associated with subsequent treatment options in patients with glioma. Study included 220 previously untreated adult patients from two institutions (94 + 126 patients) with a histopathologically confirmed diagnosis of glioma after surgery. Using a blind, cross-institutional and randomized setup, four expert neuroradiologists recorded radiologic features, suggested glioma grade and corresponding confidence. The radiologic features were scored using the Visually AcceSAble Rembrandt Images (VASARI) standard. Results were retrospectively compared to patient treatment outcomes. Our findings show that patients receiving a biopsy or a subtotal resection were more likely to have a tumor with pathological MRI-signal (by T2-weighted Fluid-Attenuated Inversion Recovery) crossing the midline (Hazard Ratio; HR = 1.30 [1.21-1.87], P < 0.001), and those receiving a biopsy sampling more often had multifocal lesions (HR = 1.30 [1.16-1.64], P < 0.001). For low-grade gliomas (N = 50), low observer confidence in the radiographic readings was associated with less chance of a total resection (P = 0.002) and correlated with the use of a more comprehensive adjuvant treatment protocol (Spearman = 0.48, P < 0.001). This study may serve as a guide to the treating physician by identifying the key radiologic determinants most likely to influence the treatment decision-making process.

Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach.

PURPOSE: According to the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) of 2016, oligodendrogliomas are now defined primarily by a specific molecular signature (presence of IDH mutation and 1p19q codeletion). The purpose of our study was to assess the value of dynamic susceptibility contrast MR imaging (DSC-MRI) and diffusion-weighted imaging (DWI) to characterize oligodendrogliomas and to distinguish them from astrocytomas. METHODS: Seventy-one adult patients with untreated WHO grade II and grade III diffuse infiltrating gliomas and known 1p/19q codeletion status were retrospectively identified and analyzed using relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps based on whole-tumor volume histograms. The Mann-Whitney U test and logistic regression were used to assess the ability of rCBV and ADC to differentiate between oligodendrogliomas and astrocytomas both independently, but also related to the WHO grade. Prediction performance was evaluated in leave-one-out cross-validation (LOOCV). RESULTS: Oligodendrogliomas showed significantly higher microvascularity (higher rCBVMean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBVPeak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADCMean ≤ 1094 × 10-6 mm2/s, p = 0.009) were more likely to be oligodendrogliomas than astrocytomas. Histogram analysis of rCBV and ADC was able to differentiate between diffuse astrocytomas (WHO grade II) and anaplastic astrocytomas (WHO grade III). CONCLUSION: Histogram-derived rCBV and ADC parameter may be used as biomarkers for identification of oligodendrogliomas and may help characterize diffuse gliomas based upon their genetic characteristics.

Survival Associations Using Perfusion and Diffusion Magnetic Resonance Imaging in Patients With Histologic and Genetic Defined Diffuse Glioma World Health Organization Grades II and III.

OBJECTIVE: According to the new World Health Organization 2016 classification for tumors of the central nervous system, 1p/19q codeletion defines the genetic hallmark that differentiates oligodendrogliomas from diffuse astrocytomas. The aim of our study was to evaluate whether relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) histogram analysis can stratify survival in adult patients with genetic defined diffuse glioma grades II and III. METHODS: Sixty-seven patients with untreated diffuse gliomas World Health Organization grades II and III and known 1p/19q codeletion status were included retrospectively and analyzed using ADC and rCBV maps based on whole-tumor volume histograms. Overall survival and progression-free survival (PFS) were analyzed by using Kaplan-Meier and Cox survival analyses adjusted for known survival predictors. RESULTS: Significant longer PFS was associated with homogeneous rCBV distribution-higher rCBVpeak (median, 37 vs 26 months; hazard ratio [HR], 3.2; P = 0.02) in patients with astrocytomas, and heterogeneous rCBV distribution-lower rCBVpeak (median, 46 vs 37 months; HR, 5.3; P < 0.001) and higher rCBVmean (median, 44 vs 39 months; HR, 7.9; P = 0.003) in patients with oligodendrogliomas. Apparent diffusion coefficient parameters (ADCpeak, ADCmean) did not stratify PFS and overall survival. CONCLUSIONS: Tumors with heterogeneous perfusion signatures and high average values were associated with longer PFS in patients with oligodendrogliomas. On the contrary, heterogeneous perfusion distribution was associated with poor outcome in patients with diffuse astrocytomas.

Risk factors for new-onset shunt-dependency after craniotomies for intracranial tumors in adult patients.

The risk of developing a de novo shunt-dependent hydrocephalus (HC) after undergoing a craniotomy for brain tumor in adult patients is largely unknown. All craniotomies for intracranial tumors at Oslo University Hospital in adult patients ≥18 years of age during a 10-year period (2004-2013) were included. None were lost to follow-up. Patients who developed a shunt-dependent HC were identified by cross-linking our prospectively collected tumor database to patients with a NCSP surgical procedure code of hydrocephalus (AAF). Patients with pre-existing HC or ventriculoperitoneal (VP) shunts were excluded from the study. A total of 4401 craniotomies were performed. Of these, 46 patients (1.0%) developed de novo postoperative HC requiring a VP shunt after a median of 93 days (mean 115 days, range 6-442). Median age was 62.0 years (mean 58.9 years, range 27.3-80.9) at time of VP shunt surgery. Patients without pre-existing HC had a 0.2% (n = 8/4401) risk of becoming VP shunt dependent within 30 days and 0.5% (n = 22/4401) within 90 days. Age, sex, tumor location, primary/secondary surgery, and radiotherapy were not associated with VP shunt dependency. Choroid plexus tumors and craniopharyngiomas had increased risk of VP shunt dependency. In this large, contemporary, single-institution consecutive series, the risk of postoperative shunt-dependency after craniotomies for brain tumors without pre-existing HC was very low. This is the largest study with regards to de novo postoperative shunt-dependency after craniotomies for patients with intracranial tumors and can serve as a benchmark for future studies.

Imaging spectrum of central nervous system complications of hematopoietic stem cell and solid organ transplantation.

Neurologic complications are common after hematopoietic stem cell transplantation (HSCT) and solid organ transplantation (SOT) and affect 30-60% of transplant recipients. The aim of this article is to provide a practical imaging approach based on the timeline and etiology of CNS abnormalities, and neurologic complications related to transplantation of specific organs. The lesions will be classified based upon the interval from HSCT procedure: pre-engraftment period <30 days, early post-engraftment period 30-100 days, late post-engraftment period >100 days, and the interval from SOT procedure: postoperative phase 1-4 weeks, early posttransplant syndromes 1-6 months, late posttransplant syndromes >6 months. Further differentiation will be based on etiology: infections, drug toxicity, metabolic derangements, cerebrovascular complications, and posttransplantation malignancies. In addition, differentiation will be based on complications specific to the type of transplantation: allogeneic and autologous hematopoietic stem cells (HSC), heart, lung, kidney, pancreas, and liver. Thus, in this article we emphasize the strategic role of neuroradiology in the diagnosis and response to treatment by utilizing a methodical approach in the work up of patients with neurologic complications after transplantation.

Benign Sphenoid Wing Meningioma Presenting with an Acute Intracerebral Hemorrhage - A Case Report.

BACKGROUND AND STUDY OBJECT: We report an unusual case of a benign lateral sphenoid wing meningioma that presented with, and was masked by, an acute intracerebral hemorrhage. CASE REPORT: A 68-year-old woman was admitted after sudden onset of coma. Computed tomography (CT) revealed an intracerebral hemorrhage, without any underlying vascular pathology on CT angiography. During the surgery, we found a lateral sphenoid wing meningioma with intratumoral bleeding that extended into the surrounding brain parenchyma. RESULTS: We removed the hematoma and resected the tumor completely in the same session. The histopathological classification of the tumor was a WHO grade I meningothelial meningioma. The patient recovered very well after surgery, without significant neurological sequelae. CONCLUSIONS: Having reviewed the relevant references from the medical literature, we consider this event as an extremely rare presentation of a benign sphenoid wing meningioma in a patient without any predisposing medical factors. The possible mechanisms of bleeding from this tumor type are discussed.

A generic support vector machine model for preoperative glioma survival associations.

PURPOSE: To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of this model in autonomous patient data. MATERIALS AND METHODS: Institutional and regional medical ethics committees approved the study, and all patients signed a consent form. Two hundred thirty-five preoperative adult patients from two institutions with a subsequent histologically confirmed diagnosis of glioma after surgery were included retrospectively. An SVM learning technique was applied to MR imaging-based whole-tumor relative cerebral blood volume (rCBV) histograms. SVM models with the highest diagnostic accuracy for 6-month and 1-, 2-, and 3-year survival associations were trained on 101 patients from the first institution. With Cox survival analysis, the diagnostic effectiveness of the SVM models was tested on independent data from 134 patients at the second institution. RESULTS: were adjusted for known survival predictors, including patient age, tumor size, neurologic status, and postsurgery treatment, and were compared with survival associations from an expert reader. RESULTS: Compared with total qualitative assessment by an expert reader, the whole-tumor rCBV-based SVM model was the strongest parameter associated with 6-month and 1-, 2-, and 3-year survival in the independent patient data (area under the receiver operating characteristic curve, 0.794-0.851; hazard ratio, 5.4-21.2). DISCUSSION: Machine learning by means of SVM in combination with whole-tumor rCBV histogram analysis can be used to identify early patient survival in aggressive gliomas. The SVM model returned higher diagnostic accuracy values than an expert reader, and the model appears to be insensitive to patient, observer, and institutional variations.

Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI.

PURPOSE: To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion-based dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI. MATERIALS AND METHODS: The study was approved by the Ethics Committee and informed consent was signed. Structural, diffusion- and perfusion-weighted MRI was performed at 1.5-T preoperatively in 94 adult patients (49 males, 45 females, 23-82 years; mean 51 years) later diagnosed with a primary glioma. Patients were randomly assigned in training and test datasets and the resulting DSC-based survival associations by SVM were compared to traditional MRI features including contrast-agent enhancement, perfusion- and diffusion-weighted imaging, tumor size, and location. The results were adjusted for age, neurological status, and postoperative factors associated with survival, including surgery and adjuvant therapy. RESULTS: For 1- (26/33 alive, 11/14 deceased), 2- (15/21, 21/26), 3- (12/16, 27/31) and 4- (12/15, 28/32) year survival associations in the test dataset (47 patients), the SVM routine was the only biomarker to consistently associate with survival (Cox; P < 0.001). CONCLUSION: The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.

Radiotherapy for spinal metastases from breast cancer with emphasis on local disease control and pain response using repeated MRI.

AIMS: To evaluate metastatic lesions within the radiation field using repeated magnetic resonance imaging (MRI) and to compare the imaging findings with pain response following radiotherapy (RT) in patients with spinal metastases (SM) from breast cancer. MATERIAL AND METHODS: 32 Patients with SM from breast cancer admitted for fractionated RT were included in this study. MRI examinations of the spine were scored for the extent of bone metastases, epidural disease and the presence and severity of vertebral fractures. Clinical response was defined according to the updated international consensus on palliative RT endpoints. RESULTS: At 2 and 6 months after RT, 38% and 44% of the patients were classified as responders. None of the patients developed motor deficits. Importantly, a decrease in the intraspinal tumor volume after RT was reported in all patients. Only 6% of the patients showed bone metastases progression within the RT field, whereas 60% of the patients showed disease progression outside the RT portals. 5 Patients developed new fractures after RT, and fracture progression was observed in 21 of the 38 lesions (55%). The pain response to RT did not correlate with the presence of vertebral body fracture before RT, fracture progression or other recorded MRI features of metastatic lesions. CONCLUSION: RT provided excellent local tumor control in patients with SM. Most patients benefit from RT even in cases of progressive vertebral fracture. Pain response was not associated with imaging findings and MRI cannot be used to select patients at risk of not responding to RT.

Limited utility of current MRI criteria for distinguishing multiple sclerosis from common mimickers: primary and secondary CNS vasculitis, lupus and Sjogren's syndrome.

BACKGROUND: Magnetic resonance imaging (MRI) criteria play an important role in making an earlier diagnosis of multiple sclerosis (MS) in patients presenting with clinically isolated syndrome. OBJECTIVE: The objective of this paper is to determine whether MRI criteria may be used to distinguish MS from primary and secondary central nervous system (CNS) vasculitis, lupus, and Sjogren's syndrome. METHODS: MRI criteria were applied retrospectively to images for patients with clinically definite MS (CDMS), primary CNS vasculitis, secondary CNS vasculitis, and autoimmune disorders including systemic lupus erythematosus (SLE) and Sjogren's syndrome. Classical statistics and Bayesian analyses were performed. RESULTS: Overall modified Barkhof's MRI criteria were statistically significant in distinguishing CDMS (60%) from SLE/Sjogren's syndrome (17%, p = 0.0173) but not in distinguishing CDMS from primary CNS vasculitis (50%, p = 0.7376) or secondary CNS vasculitis (58%, p = 1.0000). Four of the five other MRI criteria tested were demonstrated to be superior to modified Barkhof's criteria in predicting MS: nine or more T2 lesions (a component of Barkhof's criteria), one or more ovoid periventricular T2 lesions, one or more perpendicular periventricular T2 lesions, and one or more T2 lesions larger than 6 mm. CONCLUSIONS: MRI criteria, including the modified Barkhof's criteria, were unsuccessful in distinguishing MS from primary CNS vasculitis or secondary CNS vasculitis and mildly successful in distinguishing MS from SLE/Sjogren's syndrome.

The pulmonary artery in patients with Marfan syndrome: a cross-sectional study.

PURPOSE: The objectives of this study were to establish the prevalence of pulmonary artery dilatation in Marfan syndrome using modern radiological methods and to correlate the diameter of the vessel with aortic disease. METHODS: Magnetic resonance or computed tomography imaging of the pulmonary artery and aorta was performed in 87 patients with proven Marfan syndrome. Diameters of the root and trunk of the pulmonary artery and of the aortic root were measured perpendicular to the long axes of the vessels. Pulmonary artery diameters were measured on axial images, and aortic diameters were assessed on oblique sagittal images. RESULTS: As compared with normal values in the literature, 47 of the 87 patients (54%) had widening of the trunk of the pulmonary artery (≥30 mm). Of these 47, 15% had no sign of disease of the ascending aorta. The mean (SD) ratio between the diameters of the root and trunk of the pulmonary artery was 1.18 (0.155). Multivariate analysis showed that surgery of the ascending aorta and high body surface area were associated with dilatation of the trunk of the pulmonary artery. CONCLUSIONS: Pulmonary artery dilatation is present in a high proportion of patients with Marfan syndrome as assessed using cutoff values based on measurements in the normal population. Severe disease of the ascending aorta correlates significantly with pulmonary artery trunk dilatation in patients with Marfan syndrome.

Assessment of pituitary adenoma volumetric change using longitudinal MR image registration.

INTRODUCTION: Change detection is a crucial factor in monitoring of slowly evolving pathologies. The objective of the study was to test a semi-automatic method applied on longitudinal MRI monitoring of volume change in pituitary macroadenomas. METHODS: The proposed method is based on a visual comparison of geometrically corrected, co-registered, intensity-normalized contrast-enhanced (CE) 3D GRE T1-weighted images. Qualitative volume changes based on this applied method were compared with experts' readings of conventional pre- and post-CE 2D T1-weighted images. Magnetic resonance (MR) imaging was performed two to four times in 13 patients with a total combination of 29 time points. RESULTS: Compared to conventional 2D MR readings, a diagnosis of tumor growth (yes/no) was changed in 5 of 13 patients (38%) at 9 of the 29 combinations of time points (31%) using the 3D-based semi-automatic method. With manual tumor tracings as reference, McNemar's test showed a significant difference between the two methods. CONCLUSION: Visual comparison of geometrically corrected, intensity-normalized, and affine-aligned longitudinal 3D images may enable more accurate assessment of qualitative volumetric change in pituitary adenomas than conventional reading of 2D images.

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction.

BACKGROUND: A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. PURPOSE: To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. MATERIAL AND METHODS: T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. RESULTS: Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). CONCLUSION: Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.

The expression of E-cadherin in somatotroph pituitary adenomas is related to tumor size, invasiveness, and somatostatin analog response.

CONTEXT: Appropriate cell-to-cell adhesion is fundamental for the epithelial phenotype of pituitary cells. Loss of the adhesion protein E-cadherin has been associated with invasiveness, metastasis, and poor prognosis in cancers of epithelial origin. In somatotroph adenomas, a variable and reduced expression of E-cadherin has been demonstrated. In addition, nuclear translocation of E-cadherin was found to correlate with pituitary tumor invasion. OBJECTIVE: The objective was to examine the protein expression of E-cadherin in somatotroph pituitary adenomas in relation to adenoma size, invasiveness, and somatostatin analog (SMS) efficacy. PATIENTS AND METHODS: Eighty-three patients were included, and 29 were treated preoperatively with SMS. Adenoma E-cadherin protein expression was analyzed by Western blot (61 patients) and immunohistochemistry (IHC) (80 patients) with antibodies directed against both extracellular and intracellular domains (IHC). The acute (direct surgery group) and long-term (preoperatively treated group) SMS responses were evaluated. Baseline tumor volume and invasiveness were measured on magnetic resonance imaging scans. RESULTS: Membranous E-cadherin was lost in several adenomas. Nine of these were nuclear E-cadherin positive. The E-cadherin protein expression correlated negatively to tumor size and positively to acute SMS response. Low E-cadherin levels (preoperatively treated group only) and loss of membranous E-cadherin correlated to tumor invasiveness. The E-cadherin level correlated positively to tumor reduction after SMS treatment, and adenomas with nuclear E-cadherin staining had lower IGF-I reduction and tumor shrinkage. Preoperatively treated adenomas had reduced E-cadherin protein levels, but the IHC expression was unaltered. CONCLUSION: Reduced E-cadherin expression may correlate to a dedifferentiated phenotype in the somatotroph pituitary adenomas.

Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering.

PURPOSE: To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. MATERIALS AND METHODS: Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. RESULTS: The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P = 0.576-0.970). When identifying a high-risk patient group (expected survival <2 years) and a low-risk patient group (expected survival >2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P < 0.001) compared with the manual volumes (10.650-12.761; P = 0.001-0.002). CONCLUSION: Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization.

Automatic vessel removal in gliomas from dynamic susceptibility contrast imaging.

The presence of macroscopic vessels within the tumor region is a potential confounding factor in MR-based dynamic susceptibility contrast (DSC)-enhanced glioma grading. In order to distinguish between such vessels and the elevated cerebral blood volume (CBV) of brain tumors, we propose a vessel segmentation technique based on clustering of multiple parameters derived from the dynamic contrast-enhanced first-pass curve. A total of 77 adult patients with histologically-confirmed gliomas were imaged at 1.5T and glioma regions-of-interest (ROIs) were derived from the conventional MR images by a neuroradiologist. The diagnostic accuracy of applying vessel exclusion by segmentation of glioma ROIs with vessels included was assessed using a histogram analysis method and compared to glioma ROIs with vessels included. For all measures of diagnostic efficacy investigated, the highest values were observed when the glioma diagnosis was based on vessel segmentation in combination with an initial mean transit time (MTT) mask. Our results suggest that vessel segmentation based on DSC parameters may improve the diagnostic efficacy of glioma grading. The proposed vessel segmentation is attractive because it provides a mask that covers all pixels affected by the intravascular susceptibility effect.

Predictive modeling in glioma grading from MR perfusion images using support vector machines.

The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived from MR perfusion images and to assess the diagnostic accuracy of the model and the sensitivity to sample size. A total of 86 patients with histologically-confirmed gliomas were imaged using dynamic susceptibility contrast (DSC) MRI at 1.5T. Histogram signatures from 53 of the 86 patients were analyzed independently by four neuroradiologists and used as a basis for the predictive SVM model. The resulting SVM model was tested on the remaining 33 patients and analyzed by a fifth neuroradiologist. At optimal SVM parameters, the true positive rate (TPR) and true negative rate (TNR) of the SVM model on the 33 patients was 0.76 and 0.82, respectively. The interobserver agreement and the TPR increased significantly when the SVM model was based on an increasing sample size (P < 0.001). This result suggests that a predictive SVM model can aid in the diagnosis of glioma grade from MR perfusion images and that the model improves with increasing sample size.