Histogram analysis of MR imaging-derived cerebral blood volume maps: combined glioma grading and identification of low-grade oligodendroglial subtypes

Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study

PURPOSE

Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed.

RESULTS

The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894).

CONCLUSIONS

DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.

The potential role of ischaemia-reperfusion injury in chronic, relapsing diseases such as rheumatoid arthritis, Long COVID, and ME/CFS: evidence, mechanisms, and therapeutic implications

Ischaemia–reperfusion (I–R) injury, initiated via bursts of reactive oxygen species produced during the reoxygenation phase following hypoxia, is well known in a variety of acute circumstances. We argue here that I–R injury also underpins elements of the pathology of a variety of chronic, inflammatory diseases, including rheumatoid arthritis, ME/CFS and, our chief focus and most proximally, Long COVID. Ischaemia may be initiated via fibrin amyloid microclot blockage of capillaries, for instance as exercise is started; reperfusion is a necessary corollary when it finishes. We rehearse the mechanistic evidence for these occurrences here, in terms of their manifestation as oxidative stress, hyperinflammation, mast cell activation, the production of marker metabolites and related activities. Such microclot-based phenomena can explain both the breathlessness/fatigue and the post-exertional malaise that may be observed in these conditions, as well as many other observables. The recognition of these processes implies, mechanistically, that therapeutic benefit is potentially to be had from antioxidants, from anti-inflammatories, from iron chelators, and via suitable, safe fibrinolytics, and/or anti-clotting agents. We review the considerable existing evidence that is consistent with this, and with the biochemical mechanisms involved.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Assessment of Intratumor Heterogeneity in Parametric Dynamic Contrast-Enhanced MR Images: A Comparative Study of Novel and Established Methods

Intratumor heterogeneity is associated with aggressive disease and poor survival rates in several types of cancer. A novel method for assessing intratumor heterogeneity in medical images, named the spatial gradient method, has been developed in our laboratory. In this study, we measure intratumor heterogeneity in K^trans maps derived by dynamic contrast-enhanced magnetic resonance imaging using the spatial gradient method, and we compare the performance of the novel method with that of histogram analyses and texture analyses using the Haralick method. K^trans maps of 58 untreated and sunitinib-treated pancreatic ductal adenocaricoma (PDAC) xenografts from two PDAC models were investigated. Intratumor heterogeneity parameters derived by the spatial gradient method were sensitive to tumor line differences as well as sunitinib-induced changes in intratumor heterogeneity. Furthermore, the parameters provided additional information to the median value and were not severely affected by imaging noise. The parameters derived by histogram analyses were insensitive to spatial heterogeneity and were strongly correlated to the median value, and the Haralick features were severely influenced by imaging noise and did not differentiate between untreated and sunitinib-treated tumors. The spatial gradient method was superior to histogram analyses and Haralick features for assessing intratumor heterogeneity in K^trans maps of untreated and sunitinib-treated PDAC xenografts, and can possibly be used to assess intratumor heterogeneity in other medical images and to evaluate effects of other treatments as well.

Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas

The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.

Advanced imaging in adult diffusely infiltrating low-grade gliomas

The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.

Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma

OBJECTIVES

To determine whether diffusion- and perfusion-weighted MRI-based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) METHODS: Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning-based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).

RESULTS

The multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.

CONCLUSION

Multiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.

KEY POINTS

• The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.

CEST MRI with distribution-based analysis for assessment of early stage disease activity in a mouse model of multiple sclerosis: An initial study

Imaging biomarkers that can detect pathological changes at an early stage of multiple sclerosis (MS) may allow earlier therapeutic intervention with an improved outcome. Using a mouse model of MS, termed as experimental autoimmune encephalomyelitis (EAE), we performed chemical exchange saturation transfer (CEST) MRI at a very early stage before symptom onset (6 days post-induction) for assessment of changes in tissues that appear "normal" with conventional MRI. The collected CEST Z-spectra signals (S_sat /S₀ ) were analyzed using a histogram-guided method to determine the contributions from various offset frequencies. Histogram analysis showed that EAE mice exhibit a more heterogeneous distribution with lower peak heights in the hindbrain compared with naïve mice at saturation offsets of 1 and 2 ppm. At these two offsets, both the mean S_sat /S₀ and the mean MTR_asym values in the cerebellum and brain stem are significantly different between EAE and naïve mice (P < 0.05). Immunofluorescent staining validated the presence of neuroinflammation, with IBA1-positive cells detected throughout the hindbrain including the cerebellum and brain stem. Follow-up MRI at the symptom onset (score = 1.5-2.5, 13 days post-induction) confirmed gadolinium-enhanced periventricular lesions. CEST Z-spectra signals also changed by this time. The proposed three-level histogram-oriented analysis is simple to execute and robust for detecting subtle changes in Z-spectra signals, which does not require a priori knowledge of damage locations or contributing offset components. CEST MRI signals at 1 and 2 ppm were sensitive to the subtle pathological changes at an early stage in EAE mice, and have potential as novel imaging biomarkers complementary to functional and physiological MRI measures.

Usefulness of Histogram-Profile Analysis in Ring-Enhancing Intracranial Lesions

BACKGROUND

Several intracranial pathologies present as a ring-enhancing lesion on conventional magnetic resonance imaging (MRI), creating diagnostic difficulty. We studied the characteristics of the anatomical border of gadolinium enhancement on T1-weighted imaging (WI) and hypointensity on T2WI to employ a simple technique of histogram-profile analysis of MRI for differentiation of various ring-enhancing intracranial lesions.

METHODS

After approval from the institutional review board, preoperative MRI (T2WI, postcontrast T1WI) scans were analyzed retrospectively in 18 patients with histologically confirmed brain abscess, 66 glioblastomas, 46 brain-metastases, and 16 tumefactive multiple sclerosis (MS). T2WI and postcontrast T1WI were overlapped, and histogram-profile analysis was performed with in-house image-fusion software. The pattern of differential-peaks in histogram-profile was assessed visually. Kaplan-Meier survival analysis incorporating histogram-profile patterns was performed in patients with glioblastoma.

RESULTS

The histogram-profile study revealed 4 distinct patterns. Pattern 1 showed no differential T2-hypointensity trough, pattern 2 had T2-hypointensity trough inside, whereas pattern 3 had T2-hypointensity trough overlapping the enhanced margin. Pattern 4 had T2-hypointensity trough immediately external to the enhanced margin. Pattern 1 was specific for tumefactive MS (93.3%), whereas pattern 4 was specific for glioblastoma (40.7%). Pattern 4 glioblastoma was subdivided into rim (T2-hypointensity ≥50% of circumference of contrast-enhanced tumor) and arc (T2-hypointensity <50% of circumference of contrast-enhanced tumor). Pattern 4 glioblastoma was further subdivided into group A (edema: T2-hyperintensity ≥50% of circumference of contrast-enhanced tumor) and group B (less edema: T2-hyperintensity <50% of circumference of contrast-enhanced tumor). Patients with pattern 3 glioblastoma (37.6%) had better survival compared with others (P = 0.0341) and pattern 4B had decreased survival compared with pattern 4A (P = 0.0001) and others (P = 0.0003).

CONCLUSIONS

Tumefactive MS and a subset of glioblastomas show specific patterns in histogram-profile analysis. The difference in anatomical border also determines difference in survival in glioblastoma. Histogram-profile analysis is a simple and efficient technique to differentiate these pathologies.

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 rCBV_Mean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBV_Peak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADC_Mean ≤ 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.

Differentiation of grade II and III oligodendrogliomas from grade II and III astrocytomas: a histogram analysis of perfusion parameters derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI

Background Since oligodendroglial tumors are sensitive to chemotherapy and have a better prognosis, the differentiation of oligodendroglial tumors (OT) from astrocytic tumors (AT) is important. Purpose To investigate the perfusion and permeability parameters that differentiate grade II and III OT from AT, using dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI). Material and Methods We retrospectively reviewed the DCE and DSC MRIs of 39 patients with OT (OTs, n = 19; grade II, n = 12 and grade III, n = 7) and AT (ATs, n = 20; grade II, n = 7 and grade III, n = 13). Glioblastomas were not included. Various histogram parameters of relative cerebral blood volume, volume transfer constant (Ktrans), flux rate constant (Kep), plasma volume fraction (Vp), and extravascular extracellular volume fraction (Ve) from DSC and DCE MRI, were compared between the two groups. Univariable and multivariable logistic regression were used to distinguish OT from AT. Receiver operating characteristic (ROC) curve analysis was performed. Results On the results of DCE MRI, most of the histogram parameters of Ktrans, Kep, and Ve showed tendencies toward higher values in OT than AT. Multivariable logistic regression revealed that the 50th Kep and 95th Ktrans were the most significant parameters predictive of OT, with an odds ratio of 3.7 and 2.5, respectively ( P = 0.004 and 0.03). The area under the curve from the ROC curve analysis for the 50th Kep and the 95th Ktrans were 0.81 and 0.80, respectively. Conclusion The DCE MRI-derived parameters of Ktrans and Kep could facilitate differentiation of OT from AT.

Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading

PURPOSE

This study aimed to compare the tumor volume definition using conventional magnetic resonance (MR) and 11C-methionine positron emission tomography (MET/PET) images in the differentiation of the pre-operative glioma grade by using whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) maps.

MATERIALS AND METHODS

Thirty-four patients with histopathologically proven primary brain low-grade gliomas (n = 15) and high-grade gliomas (n = 19) underwent pre-operative or pre-biopsy MET/PET, fluid-attenuated inversion recovery, dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging, and contrast-enhanced T1-weighted at 3.0 T. The histogram distribution derived from the nCBV maps was obtained by co-registering the whole tumor volume delineated on conventional MR or MET/PET images, and eight histogram parameters were assessed.

RESULTS

The mean nCBV value had the highest AUC value (0.906) based on MET/PET images. Diagnostic accuracy significantly improved when the tumor volume was measured from MET/PET images compared with conventional MR images for the parameters of mean, 50th, and 75th percentile nCBV value (p = 0.0246, 0.0223, and 0.0150, respectively).

CONCLUSION

Whole-tumor histogram analysis of CBV map provides more valuable histogram parameters and increases diagnostic accuracy in the differentiation of pre-operative cerebral gliomas when the tumor volume is derived from MET/PET images.

Imaging Correlates of Adult Glioma Genotypes

Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. ^© RSNA, 2017.

Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis

BACKGROUND

DSC perfusion has been evaluated in the discrimination between low-grade and high-grade glioma but the diagnostic potential to discriminate beween glioma grades II and III remains unclear.

PURPOSE

Our aim was to evaluate the diagnostic accuracy of relative maximal CBV from DSC perfusion MR imaging to discriminate glioma grades II and III.

DATA SOURCES

A systematic literature search was performed in PubMed/MEDLINE, Embase, Web of Science, and ClinicalTrials.gov.

STUDY SELECTION

Eligible studies reported on patients evaluated with relative maximal CBV derived from DSC with a confirmed neuropathologic diagnosis of glioma World Health Organization grades II and III. Studies reporting on mean or individual patient data were considered for inclusion.

DATA ANALYSIS

Data were analyzed by using inverse variance with the random-effects model and receiver operating characteristic curves describing optimal cutoffs and areas under the curve. Bivariate diagnostic random-effects meta-analysis was used to calculate diagnostic accuracy.

DATA SYNTHESIS

Twenty-eight studies evaluating 727 individuals were included in the meta-analysis. Individual data were available from 10 studies comprising 190 individuals. The mean difference of relative maximal CBV between glioma grades II and III (n = 727) was 1.76 (95% CI, 1.27-2.24; P < .001). Individual patient data (n = 190) had an area under the curve of 0.77 for discriminating glioma grades II and III at an optimal cutoff of 2.02. When we analyzed astrocytomas separately, the area under the curve increased to 0.86 but decreased to 0.61 when we analyzed oligodendrogliomas.

LIMITATIONS

A substantial heterogeneity was found among included studies.

CONCLUSIONS

Glioma grade III had higher relative maximal CBV compared with glioma grade II. A high diagnostic accuracy was found for all patients and astrocytomas; however, the diagnostic accuracy was substantially reduced when discriminating oligodendroglioma grades II and III.

Differential diagnosis of oligodendroglial and astrocytic tumors using imaging results: the added value of perfusion MR imaging

PURPOSE

The purposes of the present study are to assess whether different characteristics of oligodendrogliomas and astrocytic tumors are visible on MR imaging and to determine the added value of perfusion imaging in conventional MR imaging when differentiating oligodendrogliomas from astrocytic tumors.

METHODS

We retrospectively studied 22 oligodendroglioma and 54 astrocytic tumor patients, including glioblastoma multiforme (GBM). The morphological tumor characteristics were evaluated using MR imaging. The rCBV, K ^trans, and V _e values were recorded. All imaging and clinical values were compared. The ability to discriminate between the two entities was evaluated using receiver operating characteristic curve analyses. Separate comparison analysis between oligodendroglioma and astrocytic tumors excluding GBM was also performed.

RESULTS

The presence of calcification, higher cortex involvement ratio, and lower V _e value were more representative of oligodendrogliomas than astrocytic tumors (P = <0.001, 0.038, and <0.001, respectively). The area under the curve (AUC) value of a combination of calcification and cortex involvement ratio was 0.796. The combination of all three parameters, including V _e, further increased the diagnostic performance (AUC = 0.881). Comparison test of the two AUC areas revealed significant difference (P = 0.0474). The presence of calcification and higher cortex involvement ratio were the only findings suggestive of oligodendrogliomas than astrocytic tumors with exclusion of GBMs (P = 0.014 and <0.001, respectively).

CONCLUSION

Cortex involvement ratio and the presence of calcification with V _e values were diagnostically accurate in identifying oligodendrogliomas. The V _e value calculated from dynamic contrast-enhanced MR imaging could be a supportive tool for differentiating between oligodendrogliomas and astrocytic tumors including GBMs.

Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges

Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.

Imaging of oligodendroglioma

Oligodendroglioma are glial tumours, predominantly occurring in adults. Their hallmark molecular feature is codeletion of the 1p and 19q chromosome arms, which is not only of diagnostic but also of prognostic and predictive relevance. On imaging, these tumours characteristically show calcification, and they have a cortical–subcortical location, most commonly in the frontal lobe. Owing to their superficial location, there may be focal thinning or remodelling of the overlying skull. In contrast to other low-grade gliomas, minimal to moderate enhancement is commonly seen and perfusion may be moderately increased. This complicates differentiation from high-grade, anaplastic oligodendroglioma, in which enhancement and increased perfusion are also common. New enhancement in a previously non-enhancing, untreated tumour, however, is suggestive of malignant transformation, as is high growth rate. MR spectroscopy may further aid in the differentiation between low- and high-grade oligodendroglioma. A relatively common feature of recurrent disease is leptomeningeal dissemination, but extraneural spread is rare. Tumours with the 1p/19q codeletion more commonly show heterogeneous signal intensity, particularly on T₂ weighted imaging; calcifications; an indistinct margin; and mildly increased perfusion and metabolism than 1p/19q intact tumours. For the initial diagnosis of oligodendroglioma, MRI and CT are complementary; MRI is superior to CT in assessing tumour extent and cortical involvement, whereas CT is most sensitive to calcification. Advanced and functional imaging techniques may aid in grading and assessing the molecular genotype as well as in differentiating between tumour recurrence and radiation necrosis, but so far no unequivocal method or combination of methods is available.

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.

The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline

QUESTION

What is the optimal imaging technique to be used in the diagnosis of a suspected low grade glioma, specifically: which anatomic imaging sequences are critical for most accurately identifying or diagnosing a low grade glioma (LGG) and do non-anatomic imaging methods and/or sequences add to the diagnostic specificity of suspected low grade gliomas?

TARGET POPULATION

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

RECOMMENDATION

LEVEL II

In patients with a suspected brain tumor, the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging. CRITICAL IMAGING FOR THE IDENTIFICATION AND DIAGNOSIS OF LOW GRADE GLIOMA:

LEVEL II

In patients with a suspected brain tumor, anatomic imaging sequences should include T1 and T2 weighted and Fluid Attenuation Inversion Recovery (FLAIR) MR sequences and will include T1 weighted imaging after the administration of gadolinium based contrast. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage, which may narrow the differential diagnosis. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention. IMPROVEMENT OF DIAGNOSTIC SPECIFICITY WITH THE ADDITION OF NON-ANATOMIC (PHYSIOLOGIC AND ADVANCED IMAGING) TO ANATOMIC IMAGING:

LEVEL II

Class II evidence from multiple studies and a significant number of Class III series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected LGGs, for the purposes of discriminating the potential for tumor subtypes and identification of suspicion of higher grade diagnoses.

LEVEL III

Multiple series offer Class III evidence to support the potential for magnetic resonance spectroscopy (MRS) and nuclear medicine methods including positron emission tomography and single-photon emission computed tomography imaging to offer additional diagnostic specificity although these are less well defined and their roles in clinical practice are still being defined.

QUESTION

Which imaging sequences or parameters best predict the biological behavior or prognosis for patients with LGG?

TARGET POPULATION

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

RECOMMENDATION

Anatomic and advanced imaging methods and prognostic stratification

LEVEL III

Multiple series suggest a role for anatomic and advanced sequences to suggest prognostic stratification among low grade gliomas. Perfusion weighted imaging, particularly when obtained as a part of diagnostic evaluation (as recommended above) can play a role in consideration of prognosis. Other imaging sequences remain investigational in terms of their role in consideration of tumor prognosis as there is insufficient evidence to support more formal recommendations as to their use at this time.

QUESTION

What is the optimal imaging technique to be used in the follow-up of a suspected (or biopsy proven) LGG?

TARGET POPULATION

This recommendation applies to adults with a newly diagnosed low grade glioma.

RECOMMENDATIONS

LEVEL II

In patients with a diagnosis of LGG, anatomic imaging sequences should include T2/FLAIR MR sequences and T1 weighted imaging before and after the administration of gadolinium based contrast. Serial imaging should be performed to identify new areas of contrast enhancement or significant change in tumor size, which may signify transformation to a higher grade.

LEVEL III

Advanced imaging utility may depend on tumor subtype. Multicenter clinical trials with larger cohorts are needed. For astrocytic tumors, baseline and longitudinal elevations in tumor perfusion as assessed by dynamic susceptibility contrast perfusion MRI are associated with shorter time to tumor progression, but can be difficult to standardize in clinical practice. For oligodendrogliomas and mixed gliomas, MRS may be helpful for identification of progression.

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors

PURPOSE

To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis.

MATERIALS AND METHODS

This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (Dt), perfusion fraction (fp), and pseudo-diffusivity (Dp) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann-Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman's rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression.

RESULTS

The average values of the ADC and IVIM biomarkers, Dt and fp, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly fp and Dp, showed significant correlation with hormonal factor expression.

CONCLUSION

Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients.

KEY POINTS

• Novel IVIM biomarkers characterize heterogeneous breast cancer. • Histogram analysis enables quantification of tumour heterogeneity. • IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.

Genetics and imaging of oligodendroglial tumors

Oligodendroglial tumors are chemosensitive with a favorable prognosis compared with other histological subtypes. The genetic hallmark of co-deletion of 1p and 19q determines both treatment response and prognosis. While this test now forms part of routine histopathology diagnosis in many laboratories, alternative noninvasive imaging biomarkers of tumor genotype remain an attractive proposition. This review will focus on imaging biomarkers of molecular genetics in oligodendroglial tumors.

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma

BACKGROUND AND PURPOSE

Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS

This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.

RESULTS

With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS

Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

Preoperative biomarkers of tumour vascularity are elevated in patients with glioblastoma multiforme

We investigated the correlation between the circulating and imaging biomarkers of tumour vascularity, and examined whether they are prognostic of outcomes in patients with glioblastoma multiforme (GBM). Despite the increasing use of anti-angiogenic agents within neuro-oncology, there are still no validated biomarkers to monitor for a treatment response or relapse. The pre- and postoperative circulating endothelial cell (CEC) and progenitor cell (CEP) levels were assessed. Preoperative perfusion-weighted MRI (PWI) was also performed, and the relative cerebral blood volume (rCBV) histogram statistics of the contrast-enhancing tumour were analysed. A novel PWI parameter (rCBVload) was developed to estimate the total volume of perfused tumour vessels, and it was hypothesised that this parameter would correlate with CEC and CEP concentrations. In total, 24 GBM patients were included. The mean preoperative CEC concentration was significantly higher in GBM patients than the controls (p=0.019), and it then declined significantly postoperatively (p=0.009). The preoperative CEP levels were significantly correlated with the median tumour rCBV (Spearman rank-order coefficient=0.526; p=0.039). Neither CEC nor CEP was correlated with the total tumour vessel volume, as measured by rCBVload. None of the biomarkers that were investigated showed a significant correlation with progression-free or overall survival. We conclude that CEC are potentially useful biomarkers to monitor GBM patients during treatment. We found that CEC are increased in the presence of GBM, and that CEP levels appear to be proportional to tumour vascularity, as measured on PWI. However, in this study, none of the biomarkers of GBM vascularity were highly prognostic of patient outcomes.

Paradoxical perfusion metrics of high-grade gliomas with an oligodendroglioma component: quantitative analysis of dynamic susceptibility contrast perfusion MR imaging

INTRODUCTION

The aim of this study is to investigate perfusion characteristics of glioblastoma with an oligodendroglioma component (GBMO) compared with conventional glioblastoma (GBM) using dynamic susceptibility contrast (DSC) perfusion magnetic resonance (MR) imaging and microvessel density (MVD).

METHODS

The study was approved by the institutional review board. Newly diagnosed high-grade glioma patients were enrolled (n = 72; 20 GBMs, 14 GBMOs, 19 anaplastic astrocytomas (AAs), 13 anaplastic oligodendrogliomas (AOs), and six anaplastic oligoastrocytomas (AOAs)). All participants underwent preoperative MR imaging including DSC perfusion MR imaging. Normalized cerebral blood volume (nCBV) values were analyzed using a histogram approach. Histogram parameters were subsequently compared across each tumor subtype and grade. MVD was quantified by immunohistochemistry staining and correlated with perfusion parameters. Progression-free survival (PFS) was assessed according to the tumor subtype.

RESULTS

GBMO displayed significantly reduced nCBV values compared with GBM, whereas grade III tumors with oligodendroglial components (AO and AOA) exhibited significantly increased nCBV values compared with AA (p < 0.001). MVD analyses revealed the same pattern as nCBV results. In addition, a positive correlation between MVD and nCBV values was noted (r = 0.633, p < 0.001). Patients with oligodendroglial tumors exhibited significantly increased PFS compared with patients with pure astrocytomas in each grade.

CONCLUSION

In contrast to grade III tumors, the presence of oligodendroglial components in grade IV tumors resulted in paradoxically reduced perfusion metrics and MVD. In addition, patients with GBMO exhibited a better clinical outcome compared with patients with GBM.

Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging

The purpose of our study was to explore the difference between isocitrate dehydrogenase (IDH)-1/2 gene mutation-positive and -negative high-grade gliomas (HGGs) using histogram analysis of apparent diffusion coefficient (ADC) and normalized cerebral blood volume (nCBV) maps. We enrolled 52 patients with histopathologically confirmed HGGs with IDH1/2 (P) (n = 16) or IDH1/2 (N) (n = 36). Histogram parameters of ADC and nCBV maps were correlated with gene mutations by using the unpaired student's t test and multivariable stepwise logistic regression analysis. The mean ADC value was higher in the IDH1 (P) group than IDH1 (N) (1,282.8 vs. 1,159.6 mm(2)/s, P = .0113). In terms of the cumulative ADC histograms, the 10th and 50th percentile values were also higher in the IDH1 (P) than IDH1 (N) (P = .0104 and .0183, respectively). We observed a higher 90th percentile value (3.121 vs. 2.397, P = .0208) and a steeper slope between the 10th (C10) and 90th (C90) of cumulative nCBV histograms (0.03386 vs. 0.02425/%, P = .0067) in the IDH1 (N) group. Multivariate analysis showed that the mean ADC mean value (P = .0048), the C90 value (P = .0113), and the slope between C10 and C90 (P = .0049) were the significant variables in the differentiation of IDH1 (P) from IDH1 (N). In conclusion, histogram analysis of ADC and nCBV maps based on entire tumor volume can be a useful tool for distinguishing IDH1 (P) and IDH1 (N), and it predicts that IDH (P) tumors have a more heterogeneous microenvironment than IDH (N) ones.

Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation

The accurate diagnosis of glioma subtypes is critical for appropriate treatment, but conventional histopathologic diagnosis often exhibits significant intra-observer variability and sampling error. The aim of this study was to investigate whether histogram analysis using an automatically segmented region of interest (ROI), excluding cystic or necrotic portions, could improve the differentiation between low-grade and high-grade gliomas. Thirty-two patients (nine low-grade and 23 high-grade gliomas) were included in this retrospective investigation. The outer boundaries of the entire tumors were manually drawn in each section of the contrast-enhanced T1 -weighted MR images. We excluded cystic or necrotic portions from the entire tumor volume. The histogram analyses were performed within the ROI on normalized apparent diffusion coefficient (ADC) maps. To evaluate the contribution of the proposed method to glioma grading, we compared the area under the receiver operating characteristic (ROC) curves. We found that an ROI excluding cystic or necrotic portions was more useful for glioma grading than was an entire tumor ROI. In the case of the fifth percentile values of the normalized ADC histogram, the area under the ROC curve for the tumor ROIs excluding cystic or necrotic portions was significantly higher than that for the entire tumor ROIs (p < 0.005). The automatic segmentation of a cystic or necrotic area probably improves the ability to differentiate between high- and low-grade gliomas on an ADC map.

Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors

PET using O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) provides important diagnostic information in addition to that from conventional MR imaging on tumor extent and activity of cerebral gliomas. Recent studies suggest that perfusion-weighted MR imaging (PWI), especially maps of regional cerebral blood volume (rCBV), may provide similar diagnostic information. In this study, we directly compared (18)F-FET PET and PWI in patients with brain tumors.

METHODS

Fifty-six patients with gliomas were investigated using static (18)F-FET PET and PWI. For comparison, 8 patients with meningiomas were included. We generated a set of tumor and reference volumes of interest (VOIs) based on morphologic MR imaging and transferred these VOIs to the corresponding (18)F-FET PET scans and PWI maps. From these VOIs, tumor-to-brain ratios (TBR) were calculated, and normalized histograms were generated for (18)F-FET PET and rCBV maps. Furthermore, in rCBV maps and in (18)F-FET PET scans, tumor volumes, their spatial congruence, and the distance between the local hot spots were assessed.

RESULTS

For patients with glioma, TBR was significantly higher in (18)F-FET PET than in rCBV maps (TBR, 2.28 ± 0.99 vs. 1.62 ± 1.13; P < 0.001). Histogram analysis of the VOIs revealed that (18)F-FET scans could clearly separate tumor from background. In contrast, deriving this information from rCBV maps was difficult. Tumor volumes were significantly larger in (18)F-FET PET than in rCBV maps (tumor volume, 24.3 ± 26.5 cm(3) vs. 8.9 ± 13.9 cm(3); P < 0.001). Accordingly, spatial overlap of both imaging parameters was poor (congruence, 11.0%), and mean distance between the local hot spots was 25.4 ± 16.1 mm. In meningioma patients, TBR was higher in rCBV maps than in (18)F-FET PET (TBR, 5.33 ± 2.63 vs. 2.37 ± 0.32; P < 0.001) whereas tumor volumes were comparable.

CONCLUSION

In patients with cerebral glioma, tumor imaging with (18)F-FET PET and rCBV yields different information. (18)F-FET PET shows considerably higher TBRs and larger tumor volumes than rCBV maps. The spatial congruence of both parameters is poor. The locations of the local hot spots differ considerably. Taken together, our data show that metabolically active tumor tissue of gliomas as depicted by amino acid PET is not reflected by rCBV as measured with PWI.

CT imaging correlates of genomic expression for oral cavity squamous cell carcinoma

BACKGROUND AND PURPOSE

Imaging correlates of genetic expression have been found for prognostic and predictive biomarkers of some malignant diseases, including breast and brain tumors. This study tests the hypothesis that imaging findings correlate with relevant genomic biomarkers in oral cavity squamous cell carcinoma.

MATERIALS AND METHODS

Surplus frozen tissue from 27 untreated patients with oral cavity squamous cell carcinoma who underwent preoperative CT imaging was analyzed for gene expression. A team of neuroradiologists blinded to the genomic analysis results reviewed an extensive list of CT findings. The imaging correlated with genomic expression for cyclin D1, angiogenesis-related genes (vascular endothelial growth factor receptors and ligands), which relate to enhancement on the basis of other tumor types; and epidermal growth factor receptor, which may relate to proliferation and mass effect.

RESULTS

Expression of vascular endothelial growth factor receptors 1 and 2 correlated with the enhancement of the primary tumor (P = .018 and P = .025, respectively), whereas the epidermal growth factor receptor correlated with mass effect (P = .03). Other exploratory correlations included epidermal growth factor receptor to perineural invasion (P = .05), and certain vascular endothelial growth factor receptors and ligands to mass effect (P = .03) and increased (P = .01) or decreased (P = .02) primary tumor size.

CONCLUSIONS

We report that CT imaging correlates with gene expression in untreated oral cavity squamous cell carcinoma. Enhancement of the primary tumor and degree of mass effect correlate with relevant genomic biomarkers, which are also potential drug targets. Eventually, treatment decisions may be aided by combining imaging findings into meaningful phenotypes that relate directly to genomic biomarkers.

Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods

Purpose

To compare the reproducibilities of manual and semiautomatic segmentation method for the measurement of normalized cerebral blood volume (nCBV) using dynamic susceptibility contrast-enhanced (DSC) perfusion MR imaging in glioblastomas.

Materials and Methods

Twenty-two patients (11 male, 11 female; 27 tumors) with histologically confirmed glioblastoma (WHO grade IV) were examined with conventional MR imaging and DSC imaging at 3T before surgery or biopsy. Then nCBV (means and standard deviations) in each mass was measured using two DSC MR perfusion analysis methods including manual and semiautomatic segmentation method, in which contrast-enhanced (CE)-T1WI and T2WI were used as structural imaging. Intraobserver and interobserver reproducibility were assessed according to each perfusion analysis method or each structural imaging. Interclass correlation coefficient (ICC), Bland-Altman plot, and coefficient of variation (CV) were used to evaluate reproducibility.

Results

Intraobserver reproducibilities on CE-T1WI and T2WI were ICC of 0.74–0.89 and CV of 20.39–36.83% in manual segmentation method, and ICC of 0.95–0.99 and CV of 8.53–16.19% in semiautomatic segmentation method, repectively. Interobserver reproducibilites on CE-T1WI and T2WI were ICC of 0.86–0.94 and CV of 19.67–35.15% in manual segmentation method, and ICC of 0.74–1.0 and CV of 5.48–49.38% in semiautomatic segmentation method, respectively. Bland-Altman plots showed a good correlation with ICC or CV in each method. The semiautomatic segmentation method showed higher intraobserver and interobserver reproducibilities at CE-T1WI-based study than other methods.

Conclusion

The best reproducibility was found using the semiautomatic segmentation method based on CE-T1WI for structural imaging in the measurement of the nCBV of glioblastomas.

Gliomas: application of cumulative histogram analysis of normalized cerebral blood volume on 3 T MRI to tumor grading

Background

Glioma grading assumes significant importance in that low- and high-grade gliomas display different prognoses and are treated with dissimilar therapeutic strategies. The objective of our study was to retrospectively assess the usefulness of a cumulative normalized cerebral blood volume (nCBV) histogram for glioma grading based on 3 T MRI.

Methods

From February 2010 to April 2012, 63 patients with astrocytic tumors underwent 3 T MRI with dynamic susceptibility contrast perfusion-weighted imaging. Regions of interest containing the entire tumor volume were drawn on every section of the co-registered relative CBV (rCBV) maps and T2-weighted images. The percentile values from the cumulative nCBV histograms and the other histogram parameters were correlated with tumor grades. Cochran’s Q test and the McNemar test were used to compare the diagnostic accuracies of the histogram parameters after the receiver operating characteristic curve analysis. Using the parameter offering the highest diagnostic accuracy, a validation process was performed with an independent test set of nine patients.

Results

The 99th percentile of the cumulative nCBV histogram (nCBV C99), mean and peak height differed significantly between low- and high-grade gliomas (P = <0.001, 0.014 and <0.001, respectively) and between grade III and IV gliomas (P = <0.001, 0.001 and <0.001, respectively). The diagnostic accuracy of nCBV C99 was significantly higher than that of the mean nCBV (P = 0.016) in distinguishing high- from low-grade gliomas and was comparable to that of the peak height (P = 1.000). Validation using the two cutoff values of nCBV C99 achieved a diagnostic accuracy of 66.7% (6/9) for the separation of all three glioma grades.

Conclusion

Cumulative histogram analysis of nCBV using 3 T MRI can be a useful method for preoperative glioma grading. The nCBV C99 value is helpful in distinguishing high- from low-grade gliomas and grade IV from III gliomas.

Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review

Susceptibility-weighted imaging (SWI) is a technique that exploits the susceptibility difference between tissues to provide contrast for different regions of the brain. In essence, it uses the deoxygenated hemoglobin of veins, hemosiderin of hemorrhage, etc. as intrinsic contrast agents, allowing for much better visualization of blood and microvessels even without administration of an external contrast agent. It is a fast-evolving field that is being constantly improved and increasingly implemented with updates in relevant technology. Multiple studies have been done on the role of SWI in the management of various neurologic disorders and it is also being seen as a further step in the neuroradiologist’s goal of being able to noninvasively grade tumors in order to influence therapy. This article briefly reviews the evolution of SWI since its conception and provides the reader with a comprehensive summary of various studies that have been done on its application for detecting and grading intraaxial brain tumors, specifically gliomas. Other useful magnetic resonance techniques that have shown promise in grading gliomas are also discussed.

Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements

OBJECTIVE

Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator.

METHODS

MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade.

RESULTS

A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis.

CONCLUSION

Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator.

ADVANCES IN KNOWLEDGE

Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.

Imaging biomarkers of angiogenesis and the microvascular environment in cerebral tumours

Conventional contrast-enhanced CT and MRI are now in routine clinical use for the diagnosis, treatment and monitoring of diseases in the brain. The presence of contrast enhancement is a proxy for the pathological changes that occur in the normally highly regulated brain vasculature and blood-brain barrier. With recognition of the limitations of these techniques, and a greater appreciation for the nuanced mechanisms of microvascular change in a variety of pathological processes, novel techniques are under investigation for their utility in further interrogating the microvasculature of the brain. This is particularly important in tumours, where the reliance on angiogenesis (new vessel formation) is crucial for tumour growth, and the resulting microvascular configuration and derangement has profound implications for diagnosis, treatment and monitoring. In addition, novel therapeutic approaches that seek to directly modify the microvasculature require more sensitive and specific biological markers of baseline tumour behaviour and response. The currently used imaging biomarkers of angiogenesis and brain tumour microvascular environment are reviewed.

Biology, genetics and imaging of glial cell tumours

Despite advances in therapy, gliomas remain associated with poor prognosis. Clinical advances will be achieved through molecularly targeted biological therapies, for which knowledge of molecular genetic and gene expression characteristics in relation to histopathology and in vivo imaging are essential. Recent research supports the molecular classification of gliomas based on genetic alterations or gene expression profiles, and imaging data supports the concept that molecular subtypes of glioma may be distinguished through non-invasive anatomical, physiological and metabolic imaging techniques, suggesting differences in the baseline biology of genetic subtypes of infiltrating glioma. Furthermore, MRI signatures are now being associated with complex gene expression profiles and cellular signalling pathways through genome-wide microarray studies using samples obtained by image guidance which may be co-registered with clinical imaging. In this review we describe the pathobiology, molecular pathogenesis, stem cells and imaging characteristics of gliomas with emphasis on astrocytomas and oligodendroglial neoplasms.

Computational analysis reveals increased blood deposition following repeated mild traumatic brain injury

Mild traumatic brain injury (mTBI) has become an increasing public health concern as subsequent injuries can exacerbate existing neuropathology and result in neurological deficits. This study investigated the temporal development of cortical lesions using magnetic resonance imaging (MRI) to assess two mTBIs delivered to opposite cortical hemispheres. The controlled cortical impact model was used to produce an initial mTBI on the right cortex followed by a second injury induced on the left cortex at 3 (rmTBI 3d) or 7 (rmTBI 7d) days later. Histogram analysis was combined with a novel semi-automated computational approach to perform a voxel-wise examination of extravascular blood and edema volumes within the lesion. Examination of lesion volume 1d post last injury revealed increased tissue abnormalities within rmTBI 7d animals compared to other groups, particularly at the site of the second impact. Histogram analysis of lesion T2 values suggested increased edematous tissue within the rmTBI 3d group and elevated blood deposition in the rm TBI 7d animals. Further quantification of lesion composition for blood and edema containing voxels supported our histogram findings, with increased edema at the site of second impact in rmTBI 3d animals and elevated blood deposition in the rmTBI 7d group at the site of the first injury. Histological measurements revealed spatial overlap of regions containing blood deposition and microglial activation within the cortices of all animals. In conclusion, our findings suggest that there is a window of tissue vulnerability where a second distant mTBI, induced 7d after an initial injury, exacerbates tissue abnormalities consistent with hemorrhagic progression.

SVM-based glioma grading: Optimization by feature reduction analysis

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.

The Role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard

BACKGROUND AND PURPOSE

Contrast extravasation in DSC-MRI potentiates inaccurate and imprecise estimates of glioma rCBV. We tested assertions that preload and postprocessing algorithms minimize this error by comparing Gd-rCBV using permutations of these 2 techniques with criterion standard rCBV using MION, an intravascular agent.

MATERIALS AND METHODS

We imaged 7 Fisher rats with 9L gliosarcomas, by using 3T gradient-echo DSC-MRI with MION (2.0 mg Fe/kg) and staged injection of Gd-diethylene triamine pentaacetic acid: a 0.1-mmol/kg bolus provided no preload (P-) data and served as preload (P+) for a subsequent 0.2-mmol/kg bolus. We computed MION-rCBV (steady-state ΔR2*, tumor versus normal brain) and Gd-rCBV ΔR2* [t] integration) without (C-) and with (C+) postprocessing correction, thereby testing 4 correction permutations: P-C-, P-C+, P+C-, and P+C+. We tested whether each permutation reduced bias and variance of the Gd/MION rCBV differences by using generalized estimating equations and Fmax statistics (P < .05 significant).

RESULTS

Gd-rCBV progressively better approximated MION-rCBV with increasing leakage correction. There was no statistically significant bias for the mean percentage deviation of Gd-rCBV from MION-rCBV for any correction permutation, but there was significantly reduced variance by using P+C- (22-fold), P-C+ (32-fold), and P+C+ (267-fold) compared with P-C-. P+C+ provided significant additional variance reduction compared with P+C- (12-fold) and P-C+ (8-fold). Linear regression of Gd-rCBV versus MION-rCBV revealed P+C+ to have the closest slope and intercept compared with the ideal, substantially better than P+C-.

CONCLUSIONS

Preload and postprocessing correction significantly reduced the variance of Gd-rCBV estimates, and bias reduction approached significance. Postprocessing correction provide significant benefit beyond preload alone.

Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas

BACKGROUND

Perfusion magnetic resonance imaging (MRI) is increasingly used in the evaluation of brain tumors. Relative cerebral blood volume (rCBV) is usually obtained by dynamic susceptibility contrast (DSC) MRI using normal appearing white matter as reference region. The emerging perfusion technique arterial spin labelling (ASL) presently provides measurement only of cerebral blood flow (CBF), which has not been widely used in human brain tumor studies.

PURPOSE

To assess if measurement of blood flow is comparable with measurement of blood volume in human biopsy-proven gliomas obtained by DSC-MRI using two different regions for normalization and two different measurement approaches.

MATERIAL AND METHODS

Retrospective study of 61 patients with different types of gliomas examined with DSC perfusion MRI. Regions of interest (ROIs) were placed in tumor portions with maximum perfusion on rCBF and rCBV maps, with contralateral normal appearing white matter and cerebellum as reference regions. Larger ROIs were drawn for histogram analyses. The type and grade of the gliomas were obtained by histopathology. Statistical comparison was made between diffuse astrocytomas, anaplastic astrocytomas, and glioblastomas.

RESULTS

rCBF and rCBV measurements obtained with the maximum perfusion method were correlated when normalized to white matter (r = 0.60) and to the cerebellum (r = 0.49). Histogram analyses of rCBF and rCBV showed that mean and median values as well as skewness and peak position were correlated (0.61 < r < 0.93), whereas for kurtosis and peak height, the correlation coefficient was about 0.3 when comparing rCBF and rCBV values for the same reference region. Neither rCBF nor rCBV quantification provided a statistically significant difference between the three types of gliomas. However, both rCBF and rCBV tended to increase with tumor grade and to be lower in patients who had undergone resection/treatment.

CONCLUSION

rCBF measurements normalized to white matter or cerebellum are comparable with the established rCBV measurements used for the clinical evaluation of cerebral gliomas.

Perfusion and permeability MR imaging of gliomas

Conventional contrast-enhanced MR imaging is the current standard technique for the diagnosis and treatment evaluation of gliomas and other brain neoplasms. However, this method is quite limited in its ability to characterize the complex biology of gliomas and so there is a need to develop more quantitative imaging methods. Perfusion and permeability MR imaging are two such techniques that have shown promise in this regard. This review will highlight the underlying principles, applications, and pitfalls of these evolving advanced MRI methods.

Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization

Dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI) is a useful method to characterize gliomas. Recently, support vector machines (SVMs) have been introduced as means to prospectively characterize new patients based on information from previous patients. Based on features derived from automatically segmented tumor volumes from 101 DSC-MR examinations, four different SVM models were compared. All SVM models achieved high prediction accuracies (>82%) after rebalancing the training data sets to equal amounts of samples per class. Best discrimination was obtained using a SVM model with a radial basis function kernel. A correct prediction of low-grade glioma was obtained at 83% (true positive rate) and for high-grade glioma at 91% (true negative rate) on the independent test data set. In conclusion, the combination of automated tumor segmentation followed by SVM classification is feasible. Thereby, a powerful tool is available to characterize glioma presurgically in patients.

Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps

INTRODUCTION

Current endeavors in neuro-oncology include morphological validation of imaging methods by histology, including molecular and immunohistochemical techniques. Diffusion tensor imaging (DTI) is an up-to-date methodology of intracranial diagnostics that has gained importance in studies of neoplasia. Our aim was to assess the feasibility of discriminant analysis applied to histograms of preoperative diffusion tensor imaging-derived images for the prediction of glioma grade validated by histomorphology.

METHODS

Tumors of 40 consecutive patients included 13 grade II astrocytomas, seven oligoastrocytomas, six grade II oligodendrogliomas, three grade III oligoastrocytomas, and 11 glioblastoma multiformes. Preoperative DTI data comprised: unweighted (B (0)) images, fractional anisotropy, longitudinal and radial diffusivity maps, directionally averaged diffusion-weighted imaging, and trace images. Sampling consisted of generating histograms for gross tumor volumes; 25 histogram bins per scalar map were calculated. The histogram bins that allowed the most precise determination of low-grade (LG) or high-grade (HG) classification were selected by multivariate discriminant analysis. Accuracy of the model was defined by the success rate of the leave-one-out cross-validation.

RESULTS

Statistical descriptors of voxel value distribution did not differ between LG and HG tumors and did not allow classification. The histogram model had 88.5% specificity and 85.7% sensitivity in the separation of LG and HG gliomas; specificity was improved when cases with oligodendroglial components were omitted.

CONCLUSION

Constructing histograms of preoperative radiological images over the tumor volume allows representation of the grade and enables discrimination of LG and HG gliomas which has been confirmed by histopathology.