Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease?

Emergent Management of Central Nervous System Demyelinating Disorders

OBJECTIVE

This article reviews the various conditions that can present with acute and severe central nervous system demyelination, the broad differential diagnosis of these conditions, the most appropriate diagnostic workup, and the acute treatment regimens to be administered to help achieve the best possible patient outcomes.

LATEST DEVELOPMENTS

The discovery of anti-aquaporin 4 (AQP4) antibodies and anti-myelin oligodendrocyte glycoprotein (MOG) antibodies in the past two decades has revolutionized our understanding of acute demyelinating disorders, their evaluation, and their management.

ESSENTIAL POINTS

Demyelinating disorders comprise a large category of neurologic disorders seen by practicing neurologists. In the majority of cases, patients with these conditions do not require care in an intensive care unit. However, certain disorders may cause severe demyelination that necessitates intensive care unit admission because of numerous simultaneous multifocal lesions, tumefactive lesions, or lesions in certain brain locations that lead to acute severe neurologic dysfunction. Intensive care may be necessary for the management and prevention of complications for patients who have severely altered mental status, rapidly progressive neurologic worsening, elevated intracranial pressure, severe cerebral edema, status epilepticus, or respiratory failure.

Brain Tumor Imaging: Review of Conventional and Advanced Techniques

Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

Current emerging MRI tools for radionecrosis and pseudoprogression diagnosis

PURPOSE OF REVIEW

This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions.

RECENT FINDINGS

Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings.

SUMMARY

Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

Tumefactive demyelination: updated perspectives on diagnosis and management

INTRODUCTION

Tumefactive demyelination (TD) can be a challenging scenario for clinicians due to difficulties distinguishing it from other conditions, such as neoplasm or infection; or with managing the consequences of acute lesions, and then deciding upon the most appropriate longer term treatment strategy.

AREAS COVERED

The authors review the literature regarding TD covering its clinic-radiological features, association with multiple sclerosis (MS), and its differential diagnosis with other neuroinflammatory and non-inflammatory mimicking disorders with an emphasis on atypical forms of demyelination including acute disseminated encephalomyelitis (ADEM), MOG antibody-associated demyelination (MOGAD) and neuromyelitis spectrum disorders (NMOSD). We also review the latest in the acute and long-term treatment of TD.

EXPERT OPINION

It is important that the underlying cause of TD be determined whenever possible to guide the management approach which differs between different demyelinating and other inflammatory conditions. Improved neuroimaging and advances in serum and CSF biomarkers should one day allow early and accurate diagnosis of TD leading to better outcomes for patients.

Physiological MRI of microvascular architecture, neovascularization activity, and oxygen metabolism facilitate early recurrence detection in patients with IDH-mutant WHO grade 3 glioma

PURPOSE

This study aimed to determine the diagnostic performance of physiological MRI biomarkers including microvascular perfusion and architecture, neovascularization activity, tissue oxygen metabolism, and tension for recurrence detection of IDH-mutant WHO grade 3 glioma.

METHODS

Sixty patients with IDH-mutant WHO grade 3 glioma who received overall 288 follow-up MRI examinations at 3 Tesla after standard treatment were retrospectively evaluated. A conventional MRI protocol was extended with a physiological MRI approach including vascular architecture mapping and quantitative blood-oxygen-level-dependent imaging which required 7 min extra data acquisition time. Custom-made MATLAB software was used for the calculation of MRI biomarker maps of microvascular perfusion and architecture, neovascularization activity, tissue oxygen metabolism, and tension. Statistical procedures included receiver operating characteristic analysis.

RESULTS

Overall, 34 patients showed recurrence of the WHO grade 3 glioma; of these, in 15 patients, recurrence was detected one follow-up examination (averaged 160 days) earlier by physiological MRI data than by conventional MRI. During this time period, the tumor volume increased significantly (P = 0.001) on average 7.4-fold from 1.5 to 11.1 cm³. Quantitative analysis of MRI biomarkers demonstrated microvascular but no macrovascular hyperperfusion in early recurrence. Neovascularization activity (AUC = 0.833), microvascular perfusion (0.682), and oxygen metabolism (0.661) showed higher diagnostic performance for early recurrence detection of WHO grade 3 glioma compared to conventional MRI including cerebral blood volume (0.649).

CONCLUSION

This study demonstrated that the targeted assessment of microvascular features and tissue oxygen tension as an early sign of neovascularization activity provided valuable information for recurrence diagnostic of WHO grade 3 glioma.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma

Glioblastoma is the most common primary brain tumor. Standard therapy consists of maximum safe resection combined with adjuvant radiochemotherapy followed by chemotherapy with temozolomide, however prognosis is extremely poor. Assessment of the residual tumor after surgery and patient stratification into prognostic groups (i.e., by tumor volume) is currently hindered by the subjective evaluation of residual enhancement in medical images (magnetic resonance imaging [MRI]). Furthermore, objective evidence defining the optimal time to acquire the images is lacking. We analyzed 144 patients with glioblastoma, objectively quantified the enhancing residual tumor through computational image analysis and assessed the correlation with survival. Pathological enhancement thickness on post-surgical MRI correlated with survival (hazard ratio: 1.98, p < 0.001). The prognostic value of several imaging and clinical variables was analyzed individually and combined (radiomics AUC 0.71, p = 0.07; combined AUC 0.72, p < 0.001). Residual enhancement thickness and radiomics complemented clinical data for prognosis stratification in patients with glioblastoma. Significant results were only obtained for scans performed between 24 and 72 h after surgery, raising the possibility of confounding non-tumor enhancement in very early post-surgery MRI. Regarding the extent of resection, and in agreement with recent studies, the association between the measured tumor remnant and survival supports maximal safe resection whenever possible.

Evaluating survival in subjects with astrocytic brain tumors by dynamic susceptibility-weighted perfusion MR imaging

Purpose

Studies have evaluated the application of perfusion MR for predicting survival in patients with astrocytic brain tumors, but few of them statistically adjust their results to reflect the impact of the variability of treatment administered in the patients. Our aim was to analyze the association between the perfusion values and overall survival time, with adjustment for various clinical factors, including initial treatments and follow-up treatments.

Materials and methods

This study consisted of 51 patients with astrocytic brain tumors who underwent perfusion-weighted MRI with MultiHance® at a dose of 0.1 mmol/kg prior to initial surgery. We measured the mean rCBV, the 5% & 10% maximum rCBV, and the variation of rCBV in the tumors. Comparisons were made between patients with and without 2-year survival using two-sample t-test or Wilcoxon rank-sum test for the continuous data, or chi-square and Fisher exact tests for categorical data. The multivariate cox-proportional hazard regression was fit to evaluate the association between rCBV and overall survival time, with adjustment for clinical factors.

Results

Patients who survived less than 2 years after diagnosis had a higher mean and maximum rCBV and a larger variation of rCBV. After adjusting for clinical factors including therapeutic measures, we found no significant association of overall survival time within 2 years with any of these rCBV values.

Conclusions

Although patients who survived less than 2 years had a higher mean and maximum rCBV and a larger variation of rCBV, rCBV itself may not be used independently for predicting 2-year survival of patients with astrocytic brain tumors.

Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers

PURPOSE

We developed multiple histogram-based CBF indices and evaluated their association with histopathologic grade in de novo brain tumor patients. Furthermore, the associations between these advanced CBF indices and molecular markers, including IDH1 mutation, ATRX loss, and 1p/19q co-deletion were also investigated.

METHODS

Thirteen de novo brain tumor patients (age 21-68 years, 9 M/4F) who were enrolled in our prospective study were scanned on 3 T MRI using a pCASL perfusion sequence following IRB-approved written informed consent. All patients have since undergone surgical intervention with tissue sampling for histopathologic tumor grading and molecular marker assessment. Tumor region of interest (ROI) were manually delineated on FLAIR images including the full extent of the tumor and peritumoral edema. Fourteen rCBF indices were derived from the histogram of the voxels with the ROI. Multi-linear regression was then used to compare rCBF indices with histopathologic tumor grade and molecular markers.

RESULTS

Averaged rCBF in top 10 and top 20 voxels (p < 0.004), but not the entire tumor ROI, was positively associated with WHO tumor grade. After accounting for tumor grade, the presence of 1p/19q co-deletion was associated with higher rCBF in top voxels, as well as with standard deviation of rCBF in the tumor ROI (p < 0.001). ATRX retention was related to higher rCBF, and this effect appears to be present in both higher-perfusion (p < 0.004) and low-perfusion (p < 0.05) voxels. IDH mutation was not significantly associated with any of the CBF indices investigated.

CONCLUSION

ASL MRI may provide useful supplemental noninvasive imaging assessment of brain tumor grade and molecular marker status.

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.

[The use of MR perfusion in assessing the efficacy of treatment for malignant brain tumors]

This literature review analyzes the capabilities of magnetic resonance imaging (MRI)-based cerebral perfusion for differentiation between post-radiation changes (e.g., radionecrosis) and continued growth. The technique is compared with other highly informative radiodiagnostic techniques used in neuroradiology. The use of MR perfusion is important in a comprehensive examination protocol. Trends in the technique development are analyzed.

Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI

Functional MRI may identify critical windows of opportunity for drug delivery and distinguish between early treatment responders and non-responders. Using diffusion-weighted, dynamic contrast-enhanced, and dynamic susceptibility contrast MRI, as well as pro-angiogenic and pro-inflammatory blood markers, we prospectively studied the physiologic tumor-related changes in fourteen newly diagnosed glioblastoma patients during standard therapy. 153 MRI scans and blood collection were performed before chemoradiation (baseline), weekly during chemoradiation (week 1–6), monthly before each cycle of adjuvant temozolomide (pre-C1-C6), and after cycle 6. The apparent diffusion coefficient, volume transfer coefficient (K^trans), and relative cerebral blood volume (rCBV) and flow (rCBF) were calculated within the tumor and edema regions and compared to baseline. Cox regression analysis was used to assess the effect of clinical variables, imaging, and blood markers on progression-free (PFS) and overall survival (OS). After controlling for additional covariates, high baseline rCBV and rCBF within the edema region were associated with worse PFS (microvessel rCBF: HR = 7.849, p = 0.044; panvessel rCBV: HR = 3.763, p = 0.032; panvessel rCBF: HR = 3.984; p = 0.049). The same applied to high week 5 and pre-C1 K^trans within the tumor region (week 5 K^trans: HR = 1.038, p = 0.003; pre-C1 K^trans: HR = 1.029, p = 0.004). Elevated week 6 VEGF levels were associated with worse OS (HR = 1.034; p = 0.004). Our findings suggest a role for rCBV and rCBF at baseline and K^trans and VEGF levels during treatment as markers of response. Functional imaging changes can differ substantially between tumor and edema regions, highlighting the variable biologic and vascular state of tumor microenvironment during therapy.

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

Objective

The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.

Methods

In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS.

Results

There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts.

Conclusions

PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

•

We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas.

•

We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis.

•

PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.

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.

Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization

PURPOSE

To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV).

METHODS

We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs).

RESULTS

GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant.

CONCLUSION

GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.

MR Imaging Features of Anaplastic Pleomorphic Xanthoastrocytoma Mimicking High-Grade Astrocytoma

BACKGROUND AND PURPOSE

Anaplastic pleomorphic xanthoastrocytoma, which has been recently defined as a distinct entity in the 2016 World Health Organization classification, may exhibit aggressive clinical behavior and relatively worse prognosis than pleomorphic xanthoastrocytoma. This study aimed to investigate whether there were any differences in MR imaging characteristics between these 2 tumors.

MATERIALS AND METHODS

This retrospective study included 9 patients with anaplastic pleomorphic xanthoastrocytoma and 10 patients with pleomorphic xanthoastrocytoma who underwent MR imaging before an operation. DWI was performed in 17 patients (8 with anaplastic pleomorphic xanthoastrocytoma, 9 with pleomorphic xanthoastrocytoma); and DSC-PWI, in 9 patients (5 with anaplastic pleomorphic xanthoastrocytoma, 4 with pleomorphic xanthoastrocytoma). Demographics, conventional imaging characteristics (location, size, cystic degeneration, enhancement, peritumoral edema, and leptomeningeal contact), minimum relative ADC ratio, and maximum relative CBV ratio were evaluated between the anaplastic pleomorphic xanthoastrocytoma and pleomorphic xanthoastrocytoma groups.

RESULTS

Anaplastic pleomorphic xanthoastrocytoma was more likely to demonstrate high-grade features than pleomorphic xanthoastrocytoma, including greater maximum tumor diameter (4.7 ± 0.6 cm versus 3.1 ± 1.1 cm, P = .001), more frequent heterogeneous contrast enhancement of solid portions (88.9% versus 20.0%, P = .01), more obvious peritumoral edema (2.3 ± 0.9 cm versus 1.0 ± 0.9 cm, P = .008), lower minimum relative ADC on DWI (1.0 ± 0.2 versus 1.5 ± 0.4, P = .008), and higher maximum relative CBV on DSC-PWI (2.6 ± 0.8 versus 1.6 ± 0.2, P = .036).

CONCLUSIONS

Anaplastic pleomorphic xanthoastrocytomas often have more aggressive MR imaging features mimicking high-grade astrocytomas than pleomorphic xanthoastrocytomas. DWI and DSC-PWI might be useful in the characterization and differentiation of anaplastic pleomorphic xanthoastrocytoma and pleomorphic xanthoastrocytoma.

The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies

OPINION STATEMENT

T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.

Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery

The current study aimed to investigate whole-brain three-dimensional arterial spin labeling imaging (3D ASL) and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), in regards to their diagnostic value of preoperative glioma grade. The parameter values obtained after correction will be correlated with the diagnostic value of 3D ASL and DSC-PWI perfusion. In the current study, 50 patients with gliomas confirmed by pathology were used, including 27 low-grade gliomas (LGGs) and 23 high-grade gliomas (HGGs). Prior to surgery all patients underwent 3 Tesla magnetic resonance imaging (MRI), 3D ASL, DSC-PWI and conventional enhanced MRI scans to obtain original 3D ASL and DSC-PWI images, and the tumor regions with the most obvious parenchyma perfusion and contralateral normal white matter were selected. In these areas, the ASL-relative cerebral blood flow (ASL-rCBF), DSC-relative cerebral blood flow (DSC-rCBF) and DSC-relative cerebral blood volume (DSC-rCBV) parameter values were then obtained after correction for individual differences. The results of the present study show that ASL-CBF, DSC-CBF, DSC-CBV values and ASL-rCBF, DSC-rCBF, DSC-rCBV values increased as the grade of the glioma being imaged increased, and there was a marked difference between the HGGs and the LGGs. ASL-rCBF was significantly positively correlated with DSC-rCBF (r=0.580, P<0.01). In addition, ASL-rCBF was significantly positively correlated with DSC-rCBV (r=0.431, P<0.01). Receiver operating characteristic (ROC) curves were applied to compare the two perfusion parameters of DSC-PWI and 3D ASL in the diagnosis of glioma grade. ASL-rCBF had the highest area value under the ROC curve (0.836). The areas under the ROC curve of DSC-rCBF and DSC-rCBV were analyzed using the Z test, but the difference was not statistically significant. When ASL-rCBF, DSC-rCBF and DSC-rCBV were cutoff at 2.24, 1.85 and 1.68, the sensitivity of HGG diagnosis was 83.2, 91.3 and 91.3%, and the specificity was 77.7, 63.9 and 66.7%, respectively.

Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution

Solid tumors are multiscale, open, complex, dynamic systems: complex because they have many interacting components, dynamic because both the components and their interactions can change with time, and open because the tumor freely communicates with surrounding and even distant host tissue. Thus, it is not surprising that striking intratumoral variations are commonly observed in clinical imaging such as MRI and CT and that several recent studies found striking regional variations in the molecular properties of cancer cells from the same tumor. Interestingly, this spatial heterogeneity in molecular properties of tumor cells is typically ascribed to branching clonal evolution due to accumulating mutations while macroscopic variations observed in, for example, clinical MRI scans are usually viewed as functions of blood flow. The clinical significance of spatial heterogeneity has not been fully determined but there is a general consensus that the varying intratumoral landscape along with patient factors such as age, morbidity and lifestyle, contributes significantly to the often unpredictable response of individual patients within a disease cohort treated with the same standard-of-care therapy.Here we investigate the potential link between macroscopic tumor heterogeneity observed by clinical imaging and spatial variations in the observed molecular properties of cancer cells. We build on techniques developed in landscape ecology to link regional variations in the distribution of species with local environmental conditions that define their habitat. That is, we view each region of the tumor as a local ecosystem consisting of environmental conditions such as access to nutrients, oxygen, and means of waste clearance related to blood flow and the local population of tumor cells that both adapt to these conditions and, to some extent, change them through, for example, production of angiogenic factors. Furthermore, interactions among neighboring habitats can produce broader regional dynamics so that the internal diversity of tumors is the net result of complex multiscale somatic Darwinian interactions.Methods in landscape ecology harness Darwinian dynamics to link the environmental properties of a given region to the local populations which are assumed to represent maximally fit phenotypes within those conditions. Consider a common task of a landscape ecologist: defining the spatial distribution of species in a large region, e.g., in a satellite image. Clearly the most accurate approach requires a meter by meter survey of the multiple square kilometers in the region of interest. However, this is both impractical and potentially destructive. Instead, landscape ecology breaks the task into component parts relying on the Darwinian interdependence of environmental properties and fitness of specific species' phenotypic and genotypic properties. First, the satellite map is carefully analyzed to define the number and distribution of habitats. Then the species distribution in a representative sampling of each habitat is empirically determined. Ultimately, this permits sufficient bridging of spatial scales to accurately predict spatial distribution of plant and animal species within large regions.Currently, identifying intratumoral subpopulations requires detailed histological and molecular studies that are expensive and time consuming. Furthermore, this method is subject to sampling bias, is invasive for vital organs such as the brain, and inherently destructive precluding repeated assessments for monitoring post-treatment response and proteogenomic evolution. In contrast, modern cross-sectional imaging can interrogate the entire tumor noninvasively, allowing repeated analysis without disrupting the region of interest. In particular, magnetic resonance imaging (MRI) provides exceptional spatial resolution and generates signals that are unique to the molecular constituents of tissue. Here we propose that MRI scans may be the equivalent of satellite images in landscape ecology and, with appropriate application of Darwinian first principles and sophisticated image analytic methods, can be used to estimate regional variations in the molecular properties of cancer cells.We have initially examined this technique in glioblastoma, a malignant brain neoplasm which is morphologically complex and notorious for a fast progression from diagnosis to recurrence and death, making a suitable subject of noninvasive, rapidly repeated assessment of intratumoral evolution. Quantitative imaging analysis of routine clinical MRIs from glioblastoma has identified macroscopic morphologic characteristics which correlate with proteogenomics and prognosis. The key to the accurate detection and forecasting of intratumoral evolution using quantitative imaging analysis is likely to be in the understanding of the synergistic interactions between observable intratumoral subregions and the resulting tumor behavior.

Quantitative measurement of cerebral blood volume using velocity-selective pulse trains

PURPOSE

To develop a non-contrast-enhanced MRI method for cerebral blood volume (CBV) mapping using velocity-selective (VS) pulse trains.

METHODS

The new pulse sequence applied velocity-sensitive gradient waveforms in the VS label modules and velocity-compensated ones in the control scans. Sensitivities to the gradient imperfections (e.g., eddy currents) were evaluated through phantom studies. CBV quantification procedures based on simulated labeling efficiencies for arteriolar, capillary, and venular blood as a function of cutoff velocity (Vc) are presented. Experiments were conducted on healthy volunteers at 3T to examine the effects of unbalanced diffusion weighting, cerebrospinal (CSF) contamination and variation of Vc.

RESULTS

Phantom results of the used VS pulse trains demonstrated robustness to eddy currents. The mean CBV values of gray matter and white matter for the experiments using Vc = 3.5 mm/s and velocity-compensated control with CSF-nulling were 5.1 ± 0.6 mL/100 g and 2.4 ± 0.2 mL/100 g, respectively, which were 23% and 32% lower than results from the experiment with velocity-insensitive control, corresponding to 29% and 25% lower in averaged temporal signal-to-noise ratio values.

CONCLUSION

A novel technique using VS pulse trains was demonstrated for CBV mapping. The results were both qualitatively and quantitatively close to those from existing methods. Magn Reson Med 77:92-101, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival

MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally,both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features(no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imaging (PWI), diffusion weighted imaging(DWI) and spectroscopy. All the gliomas had histopathological diagnoses. Sensitivity and specificity of cMRI preoperative diagnosis were 78.5 and 38.5 %, respectively, and 85.7 and 53.8 % when a MRI was included, respectively. ROC analysis showed that cut-off values of 1.29 for maximum rCBV, 1.69 for minimum rADC, 2.1 for rCho/Cr ratio could differentiate between LGG and HGG with a sensitivity of 61.5, 53.8, and 53.8 % and a specificity of 54.7, 43 and 64.3 %, respectively. A significantly longer OS was observed in patients with a maximum rCBV<1.46 and minimum rADC>1.69 (80 vs 55 months, p = 0.01; 80 vs 51 months, p = 0.002, respectively). This result was also confirmed when cases were stratified according to pathology (LGG vs HGG). The ability of a MRI to differentiate between LGG and HGG and to predict survival improved as the number of a MRI techniques considered increased. In a selected population of suspected LGG,classification by cMRI underestimated the actual fraction of HGG. aMRI slightly increased the diagnostic accuracy compared to histopathology. However, DWI and PWI were prognostic markers independent of histological grade.

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients

High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications.

Clinical PET/MR Imaging in Dementia and Neuro-Oncology

The introduction of hybrid PET/MRI systems allows simultaneous multimodality image acquisition of high technical quality. This technique is well suited for the brain, and particularly in dementia and neuro-oncology. In routine use combinations of well-established MRI sequences and PET tracers provide the most optimal and clinically valuable protocols. For dementia the [18F]-fluorodeoxyglucose (FDG) has merit with a simultaneous four sequence MRI protocol of 20 min supported by supplementary statistical reading tools and quantitative measurements of the hippocampal volume. Clinical PET/MRI using [18F]-fluoro-ethyl-tyrosine (FET) also abide to the expectations of the adaptive and versatile diagnostic tool necessary in neuro-oncology covering both simple 20 min protocols for routine treatment surveillance and complicated 90 min brain and spinal cord protocols in pediatric neuro-oncology under general anesthesia. The clinical value of adding advanced MRI sequences in multiparametric imaging setting, however, is still undocumented.

The Role of Intravoxel Incoherent Motion MRI in Predicting Early Treatment Response to Chemoradiation for Metastatic Lymph Nodes in Nasopharyngeal Carcinoma

INTRODUCTION

Pilot studies have suggested potential clinical applications for intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in head and neck cancers. This study aimed to characterize metastatic lymph nodes using IVIM MRI, and to evaluate the role of IVIM MRI in the prediction of the early treatment response of lymph node metastasis from nasopharyngeal carcinoma (NPC).

METHODS

A total of 122 patients with metastatic lymph nodes from NPC underwent two MRI examinations, pre-treatment and post-treatment (at 4 weeks and at ≥2 years from the end of chemoradiotherapy). Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1. Differences in the initial IVIM parameters [pure molecular diffusion (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f)] between nodes with a partial response (PR) and a complete response (CR) were analyzed in 102 patients after the exclusion of 20.

RESULTS

The initial D*, D, and apparent diffusion coefficient (ADC) did not reveal a significant difference between nodes showing a PR or a CR. The mean initial f value was significantly higher in patients with a PR relative to patients with a CR (p = 0.003), and its sensitivity and specificity in predicting treatment response to chemoradiotherapy were 86.7% and 100%, respectively.

CONCLUSIONS

The present study indicated that the initial f value may be more accurate than the initial D*, D, and ADC in the early prediction of treatment response to chemoradiotherapy for metastatic lymph nodes in patients with NPC.

Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma

BACKGROUND

The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (k^trans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients.

METHODS

Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS.

RESULTS

Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBV_CE for PFS, and age, sex, KPS, and EOR for OS.

CONCLUSIONS

MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBV_CE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.

Pre-Operative Perfusion Skewness and Kurtosis Are Potential Predictors of Progression-Free Survival after Partial Resection of Newly Diagnosed Glioblastoma

Objective

To determine whether pre-operative perfusion skewness and kurtosis derived from normalized cerebral blood volume (nCBV) histograms are associated with progression-free survival (PFS) of patients after partial resection of newly diagnosed glioblastoma.

Materials and Methods

A total of 135 glioblastoma patients who had undergone partial resection of tumor (resection of < 50% of pre-operative tumor volume or surgical biopsy) confirmed with immediate postsurgical MRI and examined with both conventional MRI and dynamic susceptibility contrast (DSC) perfusion MRI before the surgery were retrospectively reviewed in this study. They had been followed up post-surgical chemoradiotherapy for tumor progression. Using histogram analyses of nCBV derived from pre-operative DSC perfusion MRI, patients were sub-classified into the following four groups: positive skewness and leptokurtosis (group 1); positive skewness and platykurtosis (group 2); negative skewness and leptokurtosis (group 3); negative skewness and platykurtosis (group 4). Kaplan-Meier analysis and multivariable Cox proportional hazards regression analysis were performed to determine whether clinical and imaging covariates were associated with PFS or overall survival (OS) of these patients.

Results

According to the Kaplan-Meier method, median PFS of group 1, 2, 3, and 4 was 62, 51, 39, and 41 weeks, respectively, with median OS of 82, 77, 77, and 72 weeks, respectively. In multivariable analyses with Cox proportional hazards regression, pre-operative skewness/kurtosis pattern (hazard ratio: 2.98 to 4.64; p < 0.001), Karnofsky performance scale score (hazard ratio: 1.04; p = 0.003), and post-operative tumor volume (hazard ratio: 1.04; p = 0.02) were independently associated with PFS but not with OS.

Conclusion

Higher skewness and kurtosis of nCBV histogram before surgery were associated with longer PFS in patients with newly diagnosed glioblastoma after partial tumor resection.

Brain perfusion: computed tomography and magnetic resonance techniques

Cerebral perfusion imaging provides assessment of regional microvascular hemodynamics in the living brain, enabling in vivo measurement of a variety of different hemodynamic parameters. Perfusion imaging techniques that are used in the clinical setting usually rely upon X-ray computed tomography (CT) or magnetic resonance imaging (MRI). This chapter reviews CT- and MRI-based perfusion imaging techniques, with attention to image acquisition, clinically relevant aspects of image postprocessing, and fundamental differences between CT- and MRI-based techniques. Correlations with cerebrovascular physiology and potential clinical applications of perfusion imaging are reviewed, focusing upon the two major classes of neurologic disease in which perfusion imaging is most often performed: primary perfusion disorders (including ischemic stroke, transient ischemic attack, and reperfusion syndrome), and brain tumors.

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression

BACKGROUND AND PURPOSE

MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels.

MATERIALS AND METHODS

Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system.

RESULTS

We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P < .001). Moreover, independent of this correlation, the Cox proportional hazards model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P < .001).

CONCLUSIONS

The information of sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors.

Simultaneous evaluation of brain tumour metabolism, structure and blood volume using [(18)F]-fluoroethyltyrosine (FET) PET/MRI: feasibility, agreement and initial experience

PURPOSE

Both [(18)F]-fluoroethyltyrosine (FET) PET and blood volume (BV) MRI supplement routine T1-weighted contrast-enhanced MRI in gliomas, but whether the two modalities provide identical or complementary information is unresolved. The aims of the study were to investigate the feasibility of simultaneous structural MRI, BV MRI and FET PET of gliomas using an integrated PET/MRI scanner and to assess the spatial and quantitative agreement in tumour imaging between BV MRI and FET PET.

METHODS

A total of 32 glioma patients underwent a 20-min static simultaneous PET/MRI acquisition on a Siemens mMR system 20 min after injection of 200 MBq FET. The MRI protocol included standard structural MRI and dynamic susceptibility contrast (DSC) imaging for BV measurements. Maximal relative tumour FET uptake (TBRmax) and BV (rBVmax), and Dice coefficients were calculated to assess the quantitative and spatial congruence in the tumour volumes determined by FET PET, BV MRI and contrast-enhanced MRI.

RESULTS

FET volume and TBRmax were higher in BV-positive than in BV-negative scans, and both VOLBV and rBVmax were higher in FET-positive than in FET-negative scans. TBRmax and rBVmax were positively correlated (R (2) = 0.59, p < 0.001). FET and BV positivity were in agreement in only 26 of the 32 patients and in 42 of 63 lesions, and spatial congruence in the tumour volumes as assessed by the Dice coefficients was generally poor with median Dice coefficients exceeding 0.1 in less than half the patients positive on at least one modality for any pair of modalities. In 56 % of the patients susceptibility artefacts in DSC BV maps overlapped the tumour on MRI.

CONCLUSION

The study demonstrated that although tumour volumes determined by BV MRI and FET PET were quantitatively correlated, their spatial congruence in a mixed population of treated glioma patients was generally poor, and the modalities did not provide the same information in this population of patients. Combined imaging of brain tumour metabolism and perfusion using hybrid PET/MR systems may provide complementary information on tumour biology, but the potential clinical value remains to be determined in future trials.

Spatial distribution of malignant tissue in gliomas: correlations of 11C-L-methionine positron emission tomography and perfusion- and diffusion-weighted magnetic resonance imaging

BACKGROUND

The prognosis of glioma patients is contingent on precise target selection for stereotactic biopsies and the extent of tumor resection. (11)C-L-methionine (MET) positron emission tomography (PET) demonstrates tumor heterogeneity and invasion with high diagnostic accuracy.

PURPOSE

To compare the spatial tumor distribution delineated by MET PET with that by perfusion- and diffusion-weighted magnetic resonance imaging (MRI), in order to understand the diagnostic value of these MRI methods, when PET is not available.

MATERIAL AND METHODS

Presurgical MET PET and MRI, including perfusion- and diffusion-weighted MRI, were acquired in 13 patients (7 high-grade gliomas, 6 low-grade gliomas). A quantitative volume of interest analysis was performed to compare the modalities objectively, supplemented by a qualitative evaluation that assessed the clinical applicability.

RESULTS

The inaccuracy of conventional MRI was confirmed (area under the curve for predicting voxels with high MET uptake = 0.657), whereas cerebral blood volume (CBV) maps calculated from perfusion data improved accuracy (area under the curve = 0.760). We considered CBV maps diagnostically comparable to MET PET in 5/7 cases of high-grade gliomas, but insufficient in all cases of low-grade gliomas when evaluated subjectively. Cerebral blood flow and apparent diffusion coefficient maps did not contribute to further accuracy.

CONCLUSION

Adding perfusion-weighted MRI to the presurgical protocol can increase the diagnostic accuracy of conventional MRI and is a simple and well-established method compared to MET PET. However, the definition of low-grade gliomas with subtle or no alterations on cerebral blood volume maps remains a diagnostic challenge for stand-alone MRI.

Prognostic Value of Dynamic Susceptibility Contrast-Enhanced and Diffusion-Weighted MR Imaging in Patients with Glioblastomas

BACKGROUND AND PURPOSE

Prediction of survival in patients with glioblastomas is important for individualized treatment planning. This study aimed to assess the prognostic utility of presurgical dynamic susceptibility contrast and diffusion-weighted imaging for overall survival in patients with glioblastoma.

MATERIALS AND METHODS

MR imaging data from pathologically proved glioblastomas between June 2006 to December 2013 in 58 patients (mean age, 62.7 years; age range, 22-89 years) were included in this retrospective study. Patients were divided into long survival (≥15 months) and short survival (<15 months) groups, depending on overall survival time. Patients underwent dynamic susceptibility contrast perfusion and DWI before surgery and were treated with chemotherapy and radiation therapy. The maximum relative cerebral blood volume and minimum mean diffusivity values were measured from the enhancing part of the tumor.

RESULTS

Maximum relative cerebral blood volume values in patients with short survival were significantly higher compared with those who demonstrated long survival (P < .05). No significant difference was observed in the minimum mean diffusivity between short and long survivors. Receiver operator curve analysis demonstrated that a maximum relative cerebral blood volume cutoff value of 5.79 differentiated patients with low and high survival with an area under the curve of 0.93, sensitivity of 0.89, and specificity of 0.90 (P < .001), while a minimum mean diffusivity cutoff value of 8.35 × 10(-4)mm(2)/s had an area under the curve of 0.55, sensitivity of 0.71, and specificity of 0.47 (P > .05) in separating the 2 groups.

CONCLUSIONS

Maximum relative cerebral blood volume may be used as a prognostic marker of overall survival in patients with glioblastomas.

Pixel-by-Pixel Comparison of Volume Transfer Constant and Estimates of Cerebral Blood Volume from Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast-Enhanced MR Imaging in High-Grade Gliomas

BACKGROUND AND PURPOSE

Estimates of blood volume and volume transfer constant are parameters commonly used to characterize hemodynamic properties of brain lesions. The purposes of this study were to compare values of volume transfer constant and estimates of blood volume in high-grade gliomas on a pixel-by-pixel basis to comprehend whether they provide different information and to compare estimates of blood volume obtained by dynamic contrast-enhanced MR imaging and dynamic susceptibility contrast-enhanced MR imaging.

MATERIALS AND METHODS

Thirty-two patients with biopsy-proved grade IV gliomas underwent dynamic contrast-enhanced MR imaging and dynamic susceptibility contrast-enhanced MR imaging, and parametric maps of volume transfer constant, plasma volume, and CBV maps were calculated. The Spearman rank correlation coefficients among matching values of CBV, volume transfer constant, and plasma volume were calculated on a pixel-by-pixel basis. Comparison of median values of normalized CBV and plasma volume was performed.

RESULTS

Weak-but-significant correlation (P < .001) was noted for all comparisons. Spearman rank correlation coefficients were as follows: volume transfer constant versus CBV, ρ = 0.113; volume transfer constant versus plasma volume, ρ = 0.256; CBV versus plasma volume, ρ = 0.382. We found a statistically significant difference (P < .001) for the estimates of blood volume obtained by using dynamic contrast-enhanced MR imaging (mean normalized plasma volume, 13.89 ± 11.25) and dynamic susceptibility contrast-enhanced MR imaging (mean normalized CBV, 4.37 ± 4.04).

CONCLUSIONS

The finding of a very weak correlation between estimates of microvascular density and volume transfer constant suggests that they provide different information. Estimates of blood volume obtained by using dynamic contrast-enhanced MR imaging are significantly higher than those obtained by dynamic susceptibility contrast-enhanced MR imaging in human gliomas, most likely due to the effect of contrast leakage.

ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain

MR perfusion imaging is becoming an increasingly common means of evaluating a variety of cerebral pathologies, including tumors and ischemia. In particular, there has been great interest in the use of MR perfusion imaging for both assessing brain tumor grade and for monitoring for tumor recurrence in previously treated patients. Of the various techniques devised for evaluating cerebral perfusion imaging, the dynamic susceptibility contrast method has been employed most widely among clinical MR imaging practitioners. However, when implementing DSC MR perfusion imaging in a contemporary radiology practice, a neuroradiologist is confronted with a large number of decisions. These include choices surrounding appropriate patient selection, scan-acquisition parameters, data-postprocessing methods, image interpretation, and reporting. Throughout the imaging literature, there is conflicting advice on these issues. In an effort to provide guidance to neuroradiologists struggling to implement DSC perfusion imaging in their MR imaging practice, the Clinical Practice Committee of the American Society of Functional Neuroradiology has provided the following recommendations. This guidance is based on review of the literature coupled with the practice experience of the authors. While the ASFNR acknowledges that alternate means of carrying out DSC perfusion imaging may yield clinically acceptable results, the following recommendations should provide a framework for achieving routine success in this complicated-but-rewarding aspect of neuroradiology MR imaging practice.

Perfusion MRI derived indices of microvascular shunting and flow control correlate with tumor grade and outcome in patients with cerebral glioma

Objectives

Deficient microvascular blood flow control is thought to cause tumor hypoxia and increase resistance to therapy. In glioma patients, we tested whether perfusion-weighted MRI (PWI) based indices of microvascular flow control provide more information on tumor grade and patient outcome than does the established PWI angiogenesis marker, cerebral blood volume (CBV).

Material and Methods

Seventy-two glioma patients (sixty high-grade, twelve low-grade gliomas) were included. Capillary transit time heterogeneity (CTH) and the coefficient of variation (COV), its ratio to blood mean transit time, provide indices of microvascular flow control and the extent to which oxygen can be extracted by tumor tissue. The ability of these parameters and CBV to differentiate tumor grade were assessed by receiver operating characteristic curves and logistic regression. Their ability to predict time to progression and overall survival was examined by the Cox proportional-hazards regression model, and by survival curves using log-rank tests.

Results

The best prediction of grade (AUC = 0.876; p < 0.05) was achieved by combining knowledge of CBV and CTH in the enhancing tumor and peri-focal edema, and patients with glioblastoma multiforme were identified best by CTH (AUC = 0.763; p<0.001). CTH outperformed CBV and COV in predicting time to progression and survival in all gliomas and in a subgroup consisting of only high-grade gliomas.

Conclusion

Our study confirms the importance of microvascular flow control in tumor growth by demonstrating that determining CTH improves tumor grading and outcome prediction in glioma patients compared to CBV alone.

Survival prediction in high-grade gliomas using CT perfusion imaging

Patients with high-grade gliomas usually have heterogeneous response to surgery and chemoirradiation. The objectives of this study were (1) to evaluate serial changes in tumor volume and perfusion imaging parameters and (2) to determine the value of these data in predicting overall survival (OS). Twenty-nine patients with World Health Organization grades III and IV gliomas underwent magnetic resonance (MR) and computed tomography (CT) perfusion examinations before surgery, and 1, 3, 6, 9, and 12 months after radiotherapy. Serial measurements of tumor volumes and perfusion parameters were evaluated by receiver operating characteristic analysis, Cox proportional hazards regression, and Kaplan-Meier survival analysis to determine their values in predicting OS. Higher trends in blood flow (BF), blood volume (BV), and permeability-surface area product in the contrast-enhancing lesions (CEL) and the non-enhancing lesions (NEL) were found in patients with OS < 18 months compared to those with OS ≥ 18 months, and these values were significant at selected time points (P < 0.05). Only CT perfusion parameters yielded sensitivities and specificities of ≥ 70% in predicting 18 and 24 months OS. Pre-surgery BF in the NEL and BV in the CEL and NEL 3 months after radiotherapy had sensitivities and specificities >80% in predicting 24 months OS in patients with grade IV gliomas. Our study indicated that CT perfusion parameters were predictive of survival and could be useful in assessing early response and in selecting adjuvant treatment to prolong survival if verified in a larger cohort of patients.

Prognostic significance of dynamic 18F-FET PET in newly diagnosed astrocytic high-grade glioma

Despite advances in diagnosis and the use of different therapeutic regimens in astrocytic high-grade glioma (HGG), the prognosis for patients remains grim. Additional pretherapeutic information is needed to tailor management. To gain additional prognostic information at primary diagnosis, we investigated the value of dynamic O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) PET.

METHODS

We retrospectively evaluated 121 patients who had a primary diagnosis of astrocytic HGG (51 World Health Organization [WHO] grade III; 70 WHO IV) and underwent dynamic (18)F-FET PET before histopathologic assessment. We assessed static parameters (maximal and mean tumoral standardized uptake value corrected for mean background activity in the contralateral hemisphere [SUV(max)/BG and SUV(mean)/BG, respectively], biologic tumor volume) and dynamic time-activity curves, including minimal time to peak (TTP(min)). The prognostic influence of PET parameters and other clinical parameters on progression-free and overall survival was evaluated using uni- and multivariate Cox regression and Kaplan-Meier survival estimates.

RESULTS

In the group overall, median progression-free survival and overall survival were 12.2 and 21.9 mo. SUV(max)/BG, SUV(mean)/BG, and biologic tumor volume were significantly higher in WHO IV than in WHO III gliomas; median TTP(min) was 12.5 min in both groups. On univariate analysis, the factors age, WHO grade, O6-methylguanine-DNA methyltransferase promoter methylation status, contrast enhancement, initial treatment, and TTP(min) showed prognostic significance, with WHO grade, O6-methylguanine-DNA methyltransferase status, age, and TTP(min) remaining significant in the multivariate analysis. WHO grade and TTP(min) reached a similar fit for the prognostic evaluation. The prognosis of WHO III astrocytoma with an early TTP(min) of 12.5 min or less did not differ significantly from that of glioblastoma.

CONCLUSION

Early TTP(min) is associated with worse outcome in patients with newly diagnosed astrocytic HGG. In the preoperative setting, TTP(min) can be a valuable noninvasive prognostic marker with comparable significance to WHO grade. Additionally, TTP(min) can help identify highly aggressive WHO III astrocytoma tumors and may help in adjusting standard treatment toward an individualized, risk-adapted therapy regime.

MRI grading versus histology: predicting survival of World Health Organization grade II-IV astrocytomas

BACKGROUND AND PURPOSE

Histologic grading of intracranial astrocytomas is affected by sampling error and substantial inter- and intraobserver variability. We proposed that incorporating MR imaging into grading will predict patient survival more accurately than histopathology alone.

MATERIALS AND METHODS

Patients with a new diagnosis of World Health Organization grades II-IV astrocytoma or mixed oligoastrocytoma diagnosed between September 2007 and December 2010 were identified. Two hundred forty-five patients met the inclusion criteria. Preoperative MRIs were independently reviewed by 2 readers blinded to the histologic grade, and an MR imaging grade was given. The MR imaging and histopathologic grades were compared with patient survival.

RESULTS

Patients with grade II or III astrocytomas on histology but evidence of necrosis on MR imaging (consistent with a grade IV tumor) had significantly worse survival than patients with the same histology but no evidence of necrosis on MR imaging (P = .002 for grade II histology and P = .029 for grade III). Their survival was not significantly different from that in patients with grade IV tumors on histology (P = .164 and P = .385, respectively); this outcome suggests that all or most are likely to have truly been grade IV tumors. MR imaging evidence of necrosis was less frequent in grade II and III oligoastrocytomas, preventing adequate subgroup analysis.

CONCLUSIONS

MR imaging can improve grading of intracranial astrocytomas by identifying patients suspected of being undergraded by histology, with high interobserver agreement. This finding has the potential to optimize patient management, for example, by encouraging more aggressive treatment earlier in the patient's course.

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.

Survival analysis in patients with newly diagnosed primary glioblastoma multiforme using pre- and post-treatment peritumoral perfusion imaging parameters

The objective of this study was to evaluate if peritumoral (PT) perfusion parameters obtained from dynamic susceptibility weighted contrast enhanced perfusion MRI can predict overall survival (OS) and progression free survival (PFS) in patients with newly diagnosed glioblastoma multiforme (GBM). Twenty-eight newly diagnosed GBM patients, who were treated with resection followed by concurrent chemoradiation and adjuvant chemotherapy, were included in this study. Evaluated perfusion parameters were pre- and post-treatment PT relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF). Proportional hazard analysis was used to assess the relationship OS, PFS and perfusion parameters. Kaplan-Meier survival estimates and log-rank test were used to characterize and compare the patient groups with high and low perfusion parameter values in terms of OS and PFS. Pretreatment PT rCBV and rCBF were not associated with OS and PFS whereas there was statistically significant association of both posttreatment PT rCBV and rCBF with OS and posttreatment rCBV with PFS (association of PFS and posttreatment rCBF was not statistically significant). Neither the Kaplan-Meier survival estimates nor the log-rank test demonstrated any differences in OS between high and low pretreatment PT rCBV values and rCBF values; however, high and low post-treatment PT rCBV and rCBF values did demonstrate statistically significant difference in OS and PFS. Our study found posttreatment, not pretreatment, PT perfusion parameters can be used to predict OS and PFS in patients with newly diagnosed GBM.

Prognostic value of blood flow measurements using arterial spin labeling in gliomas

The period of event-free survival (EFS) within the same histopathological glioma grades may have high variability, mainly without a known cause. The purpose of this study was to reveal the prognostic value of quantified tumor blood flow (TBF) values obtained by arterial spin labeling (ASL) for EFS in patients with histopathologically proven astrocytomas independent of WHO (World Health Organization) grade. Twenty-four patients with untreated gliomas underwent tumor perfusion quantification by means of pulsed ASL in 3T. The clinical history of the patients was retrospectively extracted from the local database. Six patients had to be excluded due to insufficent follow-up data for further evaluation or histopathologically verified oligodendroglioma tumor components. Receiver operating characteristic (ROC) curves were used to define an optimal cut-off value of maximum TBF (mTBF) values for subgrouping in low-perfused and high-perfused gliomas. Kaplan-Meier curves and Cox proportional hazard regression model were used to determine the prognostic value of mTBF for EFS. An optimal mTBF cut-off value of 182 ml/100 g/min (sensitivity  = 83%, specificity  = 100%) was determined. Patients with low-perfused gliomas had significantly longer EFS compared to patients with high-perfused gliomas (p = 0.0012) independent of the WHO glioma grade. Quantified mTBF values obtained by ASL offer a new and totally non-invasive marker to prognosticate the EFS, independently on histopathological tumor grading, in patients with gliomas.

Spin-echo echo-planar perfusion prior to chemoradiation is a strong independent predictor of progression-free and overall survival in newly diagnosed glioblastoma

Spin-echo echo planar (EP) perfusion weighted imaging (SE-PWI) has been demonstrated to be more selective than gradient-echo EP PWI for blood volume in microvessels the size of glioma neocapillaries, but it has not been comprehensively studied in human clinical use. We assessed whether SE-PWI before and after initiating chemoradiation can stratify patients with respect to progression free survival (PFS) and overall survival (OS). Sixty-eight patients with newly diagnosed glioblastoma (mean age 58.3, 36 males) were included in analysis. SE EP cerebral blood volumes (SE-CBVs) in enhancing and nonenhancing tumor, normalized to contralateral normal appearing white matter (SE-nCBV), were assessed at baseline and after initial chemoradiation. SE-nCBV parameters predictive of PFS and OS were identified in univariate and multivariate Cox proportional hazards models. Multivariate analysis demonstrated that baseline tumor mean SE-nCBV was predictive of PFS (p = 0.038) and OS (p = 0.004). Within the patient sample, baseline tumor mean SE-nCBV <2.0 predicted longer patient PFS (median 47.0 weeks, p < 0.001) and OS (median 98.6 weeks, p = 0.003) compared with baseline mean SE-nCBV >2.0 (median PFS 25.3, median OS 56.0 weeks). Exploratory multi-group stratification demonstrated that very high (>4.0) tumor SE-nCBV was associated with worse patient OS than intermediate high (>2.0, <4.0) SE-nCBV (p = 0.025). Baseline mean SE-nCBV can stratify patients for PFS and OS prior to initiation of chemoradiation, which may help select patients who require closer surveillance. Our exploratory analysis indicates a magnitude-dependent relationship between baseline SE-nCBV and OS.

Relationship of computed tomography perfusion and positron emission tomography to tumour progression in malignant glioma

IntroductionThis study aimed to explore the potential for computed tomography (CT) perfusion and 18-Fluorodeoxyglucose positron emission tomography (FDG-PET) in predicting sites of future progressive tumour on a voxel-by-voxel basis after radiotherapy and chemotherapy.

MethodsTen patients underwent pre-radiotherapy magnetic resonance (MR), FDG-PET and CT perfusion near the end of radiotherapy and repeated post-radiotherapy follow-up MR scans. The relationships between these images and tumour progression were assessed using logistic regression. Cross-validation with receiver operating characteristic (ROC) analysis was used to assess the value of these images in predicting sites of tumour progression.

ResultsPre-radiotherapy MR-defined gross tumour; near-end-of-radiotherapy CT-defined enhancing lesion; CT perfusion blood flow (BF), blood volume (BV) and permeability-surface area (PS) product; FDG-PET standard uptake value (SUV); and SUV:BF showed significant associations with tumour progression on follow-up MR imaging (P < 0.0001). The mean sensitivity (±standard deviation), specificity and area under the ROC curve (AUC) of PS were 0.64 ± 0.15, 0.74 ± 0.07 and 0.72 ± 0.12 respectively. This mean AUC was higher than that of the pre-radiotherapy MR-defined gross tumour and near-end-of-radiotherapy CT-defined enhancing lesion (both AUCs = 0.6 ± 0.1, P ≤ 0.03). The multivariate model using BF, BV, PS and SUV had a mean AUC of 0.8 ± 0.1, but this was not significantly higher than the PS only model.

ConclusionPS is the single best predictor of tumour progression when compared to other parameters, but voxel-based prediction based on logistic regression had modest sensitivity and specificity.