Advanced MR imaging of gliomas: an update

Ultra-high b-value DWI accurately identifies isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas

OBJECTIVES

To distinguish isocitrate dehydrogenase (IDH) genotypes and tumor subtypes of adult-type diffuse gliomas based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5) in 2021 using standard, high, and ultra-high b-value diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

This prospective study enrolled 70 patients with adult-type diffuse gliomas who underwent multiple b-value DWI. Apparent diffusion coefficient (ADC) values including ADCb500/b1000, ADCb500/b2000, ADCb500/b3000, ADCb500/b4000, ADCb500/b6000, ADCb500/b8000, and ADCb500/b10000 in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The ADC ratios of TP/NAWM were assessed for correlations with IDH genotypes, tumor subtypes, and Ki-67 status; diagnostic performances were compared.

RESULTS

All ADCs were significantly higher in IDH mutant gliomas than in IDH wild-type gliomas (p < 0.01 for all); ADCb500/b8000 had the highest area under the curve (AUC) of 0.866. All ADCs were significantly lower in glioblastoma than in astrocytoma (p < 0.01 for all). ADCs other than ADCb500/b1000 were significantly lower in glioblastoma than in oligodendroglioma (p < 0.05 for all). ADCb500/b8000 and ADCb500/b10000 were significantly higher in oligodendroglioma than in astrocytoma (p = 0.034 and 0.023). The highest AUCs were 0.818 for ADCb500/b6000 when distinguishing glioblastoma from astrocytoma, 0.979 for ADCb500/b8000 and ADCb500/b10000 when distinguishing glioblastoma from oligodendroglioma, and 0.773 for ADCb500/b10000 when distinguishing astrocytoma from oligodendroglioma. Additionally, all ADCs were negatively correlated with Ki-67 status (p < 0.05 for all).

CONCLUSION

Ultra-high b-value DWI can reliably separate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using WHO CNS5 criteria.

CLINICAL RELEVANCE STATEMENT

Ultra-high b-value diffusion-weighted imaging can accurately distinguish isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas, which may facilitate personalized treatment and prognostic assessment for patients with glioma.

KEY POINTS

• Ultra-high b-value diffusion-weighted imaging can accurately distinguish subtle differences in water diffusion among biological tissues. • Ultra-high b-value diffusion-weighted imaging can reliably separate isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. • Compared with standard b-value diffusion-weighted imaging, high and ultra-high b-value diffusion-weighted imaging demonstrate better diagnostic performances.

Spatial distribution and functional relevance of FGFR1 and FGFR2 expression for glioblastoma tumor invasion

Glioblastoma is the most lethal brain cancer in adults. These incurable tumors are characterized by profound heterogeneity, therapy resistance, and diffuse infiltration. These traits have been linked to cancer stem cells, which are important for glioblastoma tumor progression and recurrence. The fibroblast growth factor receptor 1 (FGFR1) signaling pathway is a known regulator of therapy resistance and cancer stemness in glioblastoma. FGFR1 expression shows intertumoral heterogeneity and higher FGFR1 expression is associated with a significantly poorer survival in glioblastoma patients. The role of FGFR1 in tumor invasion has been studied in many cancers, but whether and how FGFR1 mediates glioblastoma invasion remains to be determined. Here, we investigated the distribution and functional relevance of FGFR1 and FGFR2 in human glioblastoma xenograft models. We found FGFR1, but not FGFR2, expressed in invasive glioblastoma cells. Loss of FGFR1, but not FGFR2, significantly reduced cell migration in vitro and tumor invasion in human glioblastoma xenografts. Comparative analysis of RNA-sequencing data of FGFR1 and FGFR2 knockdown glioblastoma cells revealed a FGFR1-specific gene regulatory network associated with tumor invasion. Our study reveals new gene candidates linked to FGFR1-mediated glioblastoma invasion.

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features

Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.

Imaging Findings and Clinical Analysis of Primary Intracranial Pure Yolk Sac Tumors in Children and Adolescents: A Retrospective Study from China

BACKGROUND AND PURPOSE

Primary intracranial pure yolk sac tumor is very rare. Our aim was to summarize the characteristics of primary intracranial pure yolk sac tumors from the clinical and imaging aspects in a retrospective study.

MATERIALS AND METHODS

We studied 5 patients with primary intracranial pure yolk sac tumors in Guangzhou Women and Children's Medical Center from January 2015 to June 2021. A comprehensive literature search was performed on the electronic database of the China National Knowledge Infrastructure (1990 to June 2021). Clinical data based on age, sex, treatment, CT, and MR imaging findings were collected and analyzed.

RESULTS

A total of 25 patients were included in the study, 21 boys and 4 girls. Twenty-one patients underwent plain MR imaging and an enhanced examination, 9 patients underwent DWI, and 12 patients underwent plain CT and/or an enhanced examination. The tumors were posterior fossa in 9 cases and supratentorial in 16 cases. All tumors showed marked enhancement after enhanced scanning by MR imaging or CT. The signal on DWI was similar to that of the cerebral cortex, and the ADC map was similar to or slightly higher than that of the cerebral cortex. Among the cases, 13 were followed up from 2 months to 5 years. There was no recurrence or metastasis in 9 patients with postoperative chemotherapy or chemoradiotherapy followed up for 1.5-5 years. Four patients died 2 months to 1.5 years after only an operation, or chemoradiotherapy but no operation.

CONCLUSIONS

There are some relatively specific imaging findings of primary intracranial yolk sac tumors that could assist in their diagnosis. Surgery combined with radiation therapy and/or chemotherapy can achieve a better prognosis.

Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI

Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging image, manual segmentation tasks can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning framework based on mutual enhancing networks to automatically segment brain tumor subregions. The proposed framework is suitable for the segmentation of brain tumor subregions owing to the contribution of Retina U-Net followed by the implementation of a mutual enhancing strategy between the classification localization map (CLM) module and segmentation module. Retina U-Net is trained to accurately identify view-of-interest and feature maps of the whole tumor (WT), which are then transferred to the CLM module and segmentation module. Subsequently, CLM generated by the CLM module is integrated with the segmentation module to bring forth a mutual enhancing strategy. In this way, our proposed framework first focuses on WT through Retina U-Net, and since WT consists of subregions, a mutual enhancing strategy then further aims to classify and segment subregions embedded within WT. We implemented and evaluated our proposed framework on the BraTS 2020 dataset consisting of 369 cases. We performed a 5-fold cross-validation on 200 datasets and a hold-out test on the remaining 169 cases. To demonstrate the effectiveness of our network design, we compared our method against the networks without Retina U-Net, mutual enhancing strategy, and a recently published Cascaded U-Net architecture. Results of all four methods were compared to the ground truth for segmentation and localization accuracies. Our method yielded significantly (P < 0.01) better values of dice-similarity-coefficient, center-of-mass-distance, and volume difference compared to all three competing methods across all tumor labels (necrosis and non-enhancing, edema, enhancing tumor, WT, tumor core) on both validation and hold-out dataset. Overall quantitative and statistical results of this work demonstrate the ability of our method to both accurately and automatically segment brain tumor subregions.

Monoexponential, biexponential and stretched exponential models of diffusion weighted magnetic resonance imaging in glioma in relation to histopathologic grade and Ki-67 labeling index using high B values

PURPOSE

To explore the performance of various parameters obtained from monoexponential (Gaussian), biexponential and stretched exponential (non-Gaussian) models of Diffusion Weighted Magnetic Resonance Imaging in differentiating gliomas with correlation to histopathology and Ki-67 labeling index (LI).

MATERIALS AND METHODS

This Institute Review Board approved retrospective study included 51 pathologically proven glioma patients (WHO Grade I, n = 1; Grade II, n = 19, Grade III, n = 12; Grade IV, n = 19), and immunohistochemistry for Ki-67 LI was obtained. The conventional Magnetic Resonance (MR) images and Diffusion Weighted (DW) images with 19 non-zero b values (0-4500 s/mm²) followed by contrast-enhanced MR images were obtained at 3T preoperatively. All images were processed with Advantage Workstation 4.5 (GE Medical Systems). Region of interest (ROI) in the solid part of the tumor was manually drawn along the border meticulously excluding areas of edema, cyst, hemorrhage, necrosis, and/or calcification, and the parameters: Apparent Diffusion Coefficient (ADC) of monoexponential; pure molecular diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) of biexponential; Distributed Diffusion Coefficient (DDC), and heterogeneity index (α) of stretched exponential models were obtained. ROI of 50 mm² in the contralateral normal appearing white matter (NAWM) was drawn for the internal control either on centrum semiovale or white matter of the frontal lobe. Analysis of reliability by Intra-class Correlation Coefficient (ICC); correlation with Ki-67 LI by Spearman's rank correlation; comparison between high grade glioma (HGG) and low grade glioma (LGG) by either Mann Whitney U test or Independent t-Test; comparison among Grade II, III and IV gliomas by one-way ANOVA with Bonferroni; and diagnostic performance by analysis of Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were conducted.

RESULTS

Highly significant differences were found between HGG and LGG for all the parameters (P < 0.001 for all). In differentiating HGG from LGG, AUC values were 0.955 for Ki-67 LI; 0.926 for α; 0.903 for Dslow; 0.897 for f; 0.863 for DDC; 0.852 for ADC; 0.820 for Dfast (P < 0.001 for all). The parameters ADC, Dslow, Dfast, f, DDC, and α showed moderate to good negative correlation with Ki-67 LI (P < 0.001 for all). The ICCs of all the parameters were found greater than 0.75 (P < 0.05 for all) suggesting good reliability of measurements.

CONCLUSION

In comparison to ADC derived from monoexponential model, the parameters α and Dslow derived from stretched exponential, and biexponential models respectively can efficiently differentiate HGG from LGG with high diagnostic accuracy. Additionally, f and DDC derived from biexponential, and stretched exponential models respectively are also more useful in differentiating HGG from LGG in comparison to ADC.

Can High b Value Diffusion Be a Surrogate Marker for PET-A MR/PET Study in Neurooncology Set Up

Purpose: Hybrid whole-body magnetic resonance/positron emission tomography (MR/PET) systems are new diagnostic tools enabling the simultaneous acquisition of morphologic and multiparametric functional data, which allow for a diversified characterization of oncological diseases. This study aimed to compare the diagnostic ability of MRI with the diffusion-weighted image (DWI), and simultaneous integrated positron emission tomography MR/PET to detect malignant lesions and elucidate the utility and limitations of these imaging modalities in preoperative and postoperative follow up in cancer patients. Material and Methods: A total of 45 patients undergoing simultaneous MR/PET for CNS ICSOL in our institution between January 2016 and July 2020 were considered in this study. Post-processing was done in Siemens syngo software to generate a b2000 image. This image was then inverted to grayscale and compared with the NAC image of PET. Results: The lesion-based sensitivity, specificity, positive predictive value, and negative predictive value for DWI were 92.3, 83.3, 97.3, and 62.5%, respectively (at 95% CI and p was 0.000). The lesion-based sensitivity, specificity, positive predictive value, and negative predictive value for PET were 97.4, 71.4, 94.9, and 83.3%, respectively (at 95% CI and p was 0.000). The lesion-based sensitivity and specificity of DWI were comparable with those of PET. Conclusions: Although DWI and FDG-PET reflect different tissue properties, there is an association between the measures of both methods in CNS tumors probably because of the coupling of cellularity with tumor metabolism as seen on FDG and other PET tracers. Our study shows that DWI acts as a surrogate biomarker for FDG PET and other tracers in tumors. The method of DWI image generation is simple, radiation-free, and cost-effective in a clinical setup. The simultaneous DWI-PET study provides evidence and confirms the role of DWI in surveillance imaging of tumors.

Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net

PURPOSE

Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice-by-slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning.

METHOD

In this study, we develop a context deep-supervised U-Net to segment brain tumor subregions. A context block which aggregates multiscale contextual information for dense segmentation was proposed. This approach enlarges the effective receptive field of convolutional neural networks, which, in turn, improves the segmentation accuracy of brain tumor subregions. We performed the fivefold cross-validation on the Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. The BraTS 2020 testing datasets were obtained via BraTS online website as a hold-out test. For BraTS, the evaluation system divides the tumor into three regions: whole tumor (WT), tumor core (TC), and enhancing tumor (ET). The performance of our proposed method was compared against two state-of-the-arts CNN networks in terms of segmentation accuracy via Dice similarity coefficient (DSC) and Hausdorff distance (HD). The tumor volumes generated by our proposed method were compared with manually contoured volumes via Bland-Altman plots and Pearson analysis.

RESULTS

The proposed method achieved the segmentation results with a DSC of 0.923 ± 0.047, 0.893 ± 0.176, and 0.846 ± 0.165 and a 95% HD95 of 3.946 ± 7.041, 3.981 ± 6.670, and 10.128 ± 51.136 mm on WT, TC, and ET, respectively. Experimental results demonstrate that our method achieved comparable to significantly (p < 0.05) better segmentation accuracies than other two state-of-the-arts CNN networks. Pearson correlation analysis showed a high positive correlation between the tumor volumes generated by proposed method and manual contour.

CONCLUSION

Overall qualitative and quantitative results of this work demonstrate the potential of translating proposed technique into clinical practice for segmenting brain tumor subregions, and further facilitate brain tumor radiotherapy workflow.

Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

RATIONALE AND OBJECTIVE

To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading.

MATERIALS AND METHODS

Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance.

RESULTS

The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade.

CONCLUSION

The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging

Purpose

Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values.

Methods

To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue.

Results

The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm³ voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm³ FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency.

Conclusion

3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints.

Preliminary evaluation of cerebral 18F-DOPA PET/CT in the differential diagnosis of brain lesions in inconclusive MR

AIM

To evaluate the utility of brain ¹⁸F-DOPA PET/CT in the differential diagnosis of brain lesions with inconclusive MRI.

MATERIAL AND METHODS

Twelve patients were studied, with a total of 16 lesions, without definitive diagnosis after brain MRI. A double acquisition PET/CT brain scan was acquired at 20 and 90min. Visual and semiquantitative assessment was performed with SUVmax calculation of the lesions and calculation of the T/S Ratio (tumor/contralateral striatum) and T/N Ratio (contralateral healthy tumor/parenchyma) for each time.

RESULTS

Based on the visual assessment scale and using T/S ratio ≥1 and T/N ratio ≥1.3 to determine malignancy, the values of sensitivity (S), specificity (E) and positive predictive value (PPV) were: visual assessment (S 100%, E 33.3%, VPP 71.4%), T/S Ratio (S 90%, E 100%, VPP 100%) and T/N Ratio (S 100%, E 16.6%, VPP 66.6 %). No lesion showed an increase in SUVmax in late acquisition. ¹⁸F-DOPA PET/CT modified treatment in 75% of the patients.

CONCLUSION

¹⁸F-DOPA PET/CT is a useful tool in the study of brain lesions with inconclusive MRI. Late imaging (dual-point) has no added value in the final diagnosis. FDOPA has an impact on patient management modifying therapeutic behavior.

Radiohistogenomics of pediatric low-grade neuroepithelial tumors

PURPOSE

In addition to histology, genetic alteration is now required to classify many central nervous system (CNS) tumors according to the most recent World Health Organization CNS tumor classification scheme. Although that is still not the case for classifying pediatric low-grade neuroepithelial tumors (PLGNTs), genetic and molecular features are increasingly being used for making treatment decisions. This approach has become a standard clinical practice in many specialized pediatric cancer centers and will likely be more widely practiced in the near future. This paradigm shift in the management of PLGNTs necessitates better understanding of how genetic alterations influence histology and imaging characteristics of individual PLGNT phenotypes.

METHODS

The complex association of genetic alterations with histology, clinical, and imaging of each phenotype of the extremely heterogeneous PLGNT family has been addressed in a holistic approach in this up-to-date review article. A new imaging stratification scheme has been proposed based on tumor morphology, location, histology, and genetics. Imaging characteristics of each PLGNT entity are also depicted in light of histology and genetics.

CONCLUSION

This article reviews the association of specific genetic alteration with location, histology, imaging, and prognosis of a specific tumor of the PLGNT family and how that information can be used for better imaging of these tumors.

Cerebral microcirculation in glioblastoma: A major determinant of diagnosis, resection, and drug delivery

Glioblastoma (GBM) is the most common primary brain tumor with a dismal prognosis. Current standard of treatment is safe maximal tumor resection followed by chemotherapy and radiation. Altered cerebral microcirculation and elevated blood-tumor barrier (BTB) permeability in tumor periphery due to glioma-induced vascular dysregulation allow T1 contrast-enhanced visualization of resectable tumor boundaries. Newer tracers that label the tumor and its vasculature are being increasingly used for intraoperative delineation of glioma boundaries for even more precise resection. Fluorescent 5-aminolevulinic acid (5-ALA) and indocyanine green (ICG) are examples of such intraoperative tracers. Recently, magnetic resonance imaging (MRI)-based MR thermometry is being employed for laser interstitial thermal therapy (LITT) for glioma debulking. However, aggressive, fatal recurrence always occurs. Postsurgical chemotherapy is hampered by the inability of most drugs to cross the blood-brain barrier (BBB). Understanding postsurgical changes in brain microcirculation and permeability is crucial to improve chemotherapy delivery. It is important to understand whether any microcirculatory indices can differentiate between true recurrence and radiation necrosis. LITT leads to peri-ablation BBB opening that persists for several weeks. Whether it can be a conduit for chemotherapy delivery is yet to be explored. This review will address the role of cerebral microcirculation in such emerging ideas in GBM diagnosis and therapy.

Unilateral posterior reversible encephalopathy syndrome characterized with a long and gradually exacerbating course over 3 years and that presented propofol infusion syndrome - A case report

Background:

Posterior reversible encephalopathy syndrome (PRES) is characterized by acute neurological symptoms and vasogenic edema, and most patients wholly recover. We report a unilateral PRES patient characterized by a gradual onset followed by propofol infusion syndrome (PRIS) due to general anesthesia therapy.

Case Description:

A 32-year-old woman had ovarian dysfunction treated by Kaufmann’s treatment for 17 years. Three years ago, she developed seizures, and photophobia and myoclonus sometimes occurred. This time, she had strong photophobia and nausea for 3 months and then developed tonic-clonic seizures for 3 min. Her blood pressure and laboratory test on admission were all within normal limits. She presented no neurological deficits at admission, but the T2-weighted image (T2WI) showed a high-intensity area (HIA), and arterial spin labeling (ASL) image described cerebral blood flow (CBF) increase in the left parieto-occipital region. We diagnosed PRES and started anticonvulsants, antihypertensive, and steroid pulse therapy. However, her aphasia and neuroimaging findings worsened, so we started general anesthesia treatment with propofol on day 29. On day 32, she suddenly developed multiple organ dysfunctions due to PRIS. After intensive care with other sedatives over 2 months, the systemic status and neurological symptoms gradually improved almost as before the onset. On day 90, HIA in the T2WI in the lesion became small, and CBF was severely downregulated in the ASL image.

Conclusion:

Unilateral PRES’s pathophysiology and the association with the female hormone remain unknown. Some patients undergo gradual onset and long-term courses, and we should care for PRIS during PRES treatment.

3D Printing of Diffuse Low-Grade Gliomas Involving Eloquent Cortical Areas and Subcortical Functional Pathways: Technical Note

BACKGROUND

Surgical resection of diffuse low-grade gliomas (DLGGs) involving cortical eloquent areas and subcortical functional pathways represents a challenge in neurosurgery. Patient-specific, 3-dimensional (3D)-printed models of head and brain structures have emerged in recent years as an educational and clinical tool for patients, doctors, and surgical residents.

METHODS

Using multimodal high-definition magnetic resonance imaging data, which incorporates information from specific task-based functional neuroimaging and diffusion tensor imaging tractography and rapid prototyping technologies with specialized software and "in-house" 3D printing, we were able to generate 3D-printed head models that were used for preoperative patient education and consultation, surgical planning, and resident training in 2 complicated DLGG surgeries.

RESULTS

This 3D-printed model is rapid prototyped and shows a means to model individualized, diffuse, low-level glioma in 3D space with respect to cortical eloquent areas and subcortical pathways. Survey results from 8 surgeons with different levels of expertise strongly support the use of this model for surgical planning, intraoperative surgical guidance, doctor-patient communication, and surgical training (>95% acceptance).

CONCLUSIONS

Spatial proximity of DLGG to cortical eloquent areas and subcortical tracts can be readily assessed in patient-specific 3D printed models with high fidelity. 3D-printed multimodal models could be helpful in preoperative patient consultation, surgical planning, and resident training.

Valoración preliminar de la 18F-DOPA PET/TC cerebral en el diagnóstico diferencial de lesiones cerebrales con RM no concluyente

AIM

To evaluate the utility of brain 18F-DOPA PET/CT in the differential diagnosis of brain lesions with inconclusive MRI.

MATERIAL AND METHODS

Twelve patients were studied, with a total of 16 lesions, without definitive diagnosis after brain MRI. A double acquisition PET/CT brain scan was acquired at 20 and 90 minutes. Visual and semiquantitative assessment was performed with SUVmax calculation of the lesions and calculation of the T/S ratio (tumor/contralateral striatum) and T/N ratio (tumor/contralateral healthy parenchyma) for each time.

RESULTS

Based on the visual assessment scale and using T/S ratio ≥ 1 and T/N ratio ≥ 1.3 to determine malignancy, the values of sensitivity (S), specificity (E) and positive predictive value (PPV) were: visual assessment (S 100%, E 33.3%, VPP 71.4%), T/S ratio (S 90%, E 100%, VPP 100%) and T/N ratio (S 100%, E 16.6%, VPP 66.6%). No lesion showed an increase in SUVmax in late acquisition. 18F-DOPA PET/CT modified treatment in 75% of the patients.

CONCLUSION

18F-DOPA PET/CT is a useful tool in the study of brain lesions with inconclusive MRI. Late imaging (dual-point) has no added value in the final diagnosis. F-DOPA has an impact on patient management modifying therapeutic behavior.

Differentiating Nonenhancing Grade II Gliomas from Grade III Gliomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI

BACKGROUND

Contrast enhancement in a brain tumor on magnetic resonance imaging is typically indicative of a high-grade glioma. However, a significant proportion of nonenhancing gliomas can be either grade II or III. While gross total resection remains the primary goal, imaging biomarkers may guide management when surgery is not possible, especially for nonenhancing gliomas. The utility of diffusion tensor imaging and dynamic susceptibility contrast magnetic resonance imaging was evaluated in differentiating nonenhancing gliomas.

METHODS

Retrospective analysis was performed on imaging data from 72 nonenhancing gliomas, including grade II (n = 49) and III (n = 23) gliomas. Diffusion tensor imaging and dynamic susceptibility contrast data were used to generate fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity as well as cerebral blood volume, cerebral blood flow, and mean transit time maps. Univariate and multivariate logistic regression and area under the curve analyses were used to measure sensitivity and specificity of imaging parameters. A subanalysis was performed to evaluate the utility of imaging parameters in differentiating between different histologic groups.

RESULTS

Logistic regression analysis indicated that tumor volume and relative mean transit time could differentiate between grade II and III nonenhancing gliomas. At a cutoff value of 0.33, this combination provided an area under the curve of 0.71, 70.6% sensitivity, and 64.3% specificity. Logistic regression analyses demonstrated much higher sensitivity and specificity in the differentiation of astrocytomas from oligodendrogliomas or identification of grades within these histologic subtypes.

CONCLUSIONS

Diffusion tensor imaging and dynamic susceptibility contrast imaging can aid in differentiation of nonenhancing grade II and III gliomas and between histologic subtypes.

Penta-fluorophenol: a Smiles rearrangement-inspired cysteine-selective fluorescent probe for imaging of human glioblastoma

Two of the most critical factors for the survival of glioblastoma (GBM) patients are precision diagnosis and the tracking of treatment progress. At the moment, various sophisticated and specific diagnostic procedures are being used, but there are relatively few simple diagnosis methods. This work introduces a sensing probe based on a turn-on type fluorescence response that can measure the cysteine (Cys) level, which is recognized as a new biomarker of GBM, in human-derived cells and within on-site human clinical biopsy samples. The Cys-initiated chemical reactions of the probe cause a significant fluorescence response with high selectivity, high sensitivity, a fast response time, and a two-photon excitable excitation pathway, which allows the imaging of GBM in both mouse models and human tissue samples. The probe can distinguish the GBM cells and disease sites in clinical samples from individual patients. Besides, the probe has no short or long-term toxicity and immune response. The present findings hold promise for application of the probe to a relatively simple and straightforward following of GBM at clinical sites.

Propofol Inhibits Cell Proliferation, Migration, and Invasion via mir-410-3p/Transforming Growth Factor-β Receptor Type 2 (TGFBR2) Axis in Glioma

BACKGROUND Propofol is a common intravenous anesthetic used to induce and maintain anesthesia. Numerous studies have reported that propofol plays an anti-tumor role in diverse human cancers, including glioma. In this research, we explored the roles of propofol and its related molecular mechanisms in glioma. MATERIAL AND METHODS U251 and A172 cells were exposed to different doses of propofol for 24 h. Cell proliferation, migration, and invasion in glioma were evaluated using MTT assay and Transwell assay, respectively. The levels of microRNA-410-3p (miR-410-3p) and transforming growth factor-ß receptor type 2 (TGFBR2) were detected by quantitative real-time polymerase chain reaction (qRT-PCR) assay and Western blot assay, respectively. The association between miR-410-3p and TGFBR2 was predicted by TargetScan and confirmed by dual-luciferase reporter assay. RESULTS Propofol inhibited the proliferation, migration, and invasion of glioma cells in a concentration-dependent way. miR-410-3p was induced and TGFBR2 was inhibited by different concentrations of propofol treatment. Moreover, TGFBR2 was confirmed to be a target gene of miR-410-3p and TGFBR2 was inversely modulated by miR-410-3p in glioma cells. Depletion of miR-410-3p reversed the inhibition of propofol treatment on U251 and A172 cell growth and metastasis, but the effects were further abolished by knocking down the expression of TGFBR2. CONCLUSIONS Propofol can suppress cell growth and metastasis by regulating the miR-410-3p/TGFBR2 axis in glioma.

A Mistaken Diagnosis of Secondary Glioblastoma as Parasitosis

Background: Glioblastoma is a malignant brain tumor with poor prognosis requiring early diagnosis. Secondary glioblastoma refers to cases that progressed from low-grade glioma. Evidence shows that timely resection correlates with increased survival.

Case presentation: We describe a case of a patient with secondary glioblastoma who was mistakenly diagnosed with Angiostrongylus cantonensis infection until 7 years after disease onset. The patient presented with non-specific clinical manifestations at disease onset. A conventional magnetic resonance imaging (MRI) in the primary survey provided insufficient information, and thus failed to identify the malignancy. During follow-up, unfortunately, clinicians were misled by the patient's raw food diet, a positive serum parasite antibody and a result of low glucose metabolism on Fluorodeoxyglucose-positron emission tomography-computed tomography (FDG-PET-CT). The patient was diagnosed with parasitosis. However, his condition kept getting worse under antiparasitic treatment. Preoperative magnetic resonance spectroscopy (MRS) and diffusion tensor imaging (DTI) failed to reverse the mistaken impression. Final diagnosis was confirmed until intraoperative and postoperative pathological findings indicated glioblastoma.

Conclusion: We ascribe the incorrect diagnosis to insufficient understanding on imaging manifestations of brain neoplasm as well as clinical features of parasitosis. Thus, we review the MRI, FDG-PET-CT, MRS, and DTI data of this case according to the timeline, refer to relevant studies, and point out the pitfalls. With a long course of slowly progressing, this was a rare case of secondary glioblastoma with the absence of isocitrate dehydrogenase 1 (IDH1) gene mutation.

A Critical Appraisal of the Quality of Glioma Imaging Guidelines Using the AGREE II Tool: A EuroAIM Initiative

Background: Following the EuroAIM initiative to assess the quality of medical imaging guidelines by using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, we aimed to evaluate the quality of the current imaging guidelines in patients with gliomas.

Methods: A literature search was conducted to identify eligible imaging guidelines considered in the management of adult patients with gliomas. The selected guidelines were evaluated using the AGREE II instrument by four independent appraisers. The agreement among the four appraisers was estimated using the intraclass correlation coefficient (ICC) analysis.

Results: Seven guidelines were selected for the appraisal. Six out of the seven guidelines showed an average level of quality with only one showing a low quality. The highest scores were found in Domain 1 “Scope and purpose” (mean score = 81.2%) and Domain 4 “Clarity of presentation” (mean score = 77.6%). The remaining domains showed a low level of quality and, in particular, Domain 5 “Applicability” was the most critical with a mean score of 41.7%, mainly related to a minor attention to barriers and facilitators as well as costs and resources implications of applying the guidelines. The ICC analysis showed a very good agreement among the four appraisers with ICC values ranging from 0.907 to 0.993.

Conclusions: The available guidelines on glioma imaging emerged as of average quality according to the AGREE II tool analysis. Based on these results, further efforts should be made in order to involve different professional bodies and stakeholders and increase patient and public involvement in any future guideline drafting as well as to improve the applicability of these guidelines into the clinical practice.

Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

Subtle tissue deformations caused by mass-effect in Glioblastoma (GBM) are often not visually evident, and may cause neurological deficits, impacting survival. Radiomic features provide sub-visual quantitative measures to uncover disease characteristics. We present a new radiomic feature to capture mass effect-induced deformations in the brain on Gadolinium-contrast (Gd-C) T1w-MRI, and their impact on survival. Our rationale is that larger variations in deformation within functionally eloquent areas of the contralateral hemisphere are likely related to decreased survival. Displacements in the cortical and subcortical structures were measured by aligning the Gd-C T1w-MRI to a healthy atlas. The variance of deformation magnitudes was measured and defined as Mass Effect Deformation Heterogeneity (MEDH) within the brain structures. MEDH values were then correlated with overall-survival of 89 subjects on the discovery cohort, with tumors on the right (n = 41) and left (n = 48) cerebral hemispheres, and evaluated on a hold-out cohort (n = 49 subjects). On both cohorts, decreased survival time was found to be associated with increased MEDH in areas of language comprehension, social cognition, visual perception, emotion, somato-sensory, cognitive and motor-control functions, particularly in the memory areas in the left-hemisphere. Our results suggest that higher MEDH in functionally eloquent areas of the left-hemisphere due to GBM in the right-hemisphere may be associated with poor-survival.

High-Grade Gliomas in Children with Neurofibromatosis Type 1: Literature Review and Illustrative Cases

High-grade gliomas in patients with neurofibromatosis type 1 are rare and may therefore not be considered in the differential of brain lesions. Here, we describe 5 children with neurofibromatosis type 1; four of them developed various types of high-grade gliomas. The fifth patient had imaging features concerning for a high-grade lesion, but tissue diagnosis showed a low-grade glioma. The cases and literature summary provided here are to raise awareness for the occurrence of high-grade gliomas in children with neurofibromatosis type 1 and the limited ability of imaging features alone to predict a high-grade malignancy.

Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas

PURPOSE

To assess the feasibility of using diffusion-weighted imaging (DWI) with the stretched-exponential model (SEM) for glioma grading and determining the correlations among parameters and proliferating cell nuclear antigen and Ki-67 expression.

MATERIALS AND METHODS

Mono-exponential model-DWI (MEM-DWI) and SEM-DWI were performed in 104 patients with pathologically proven gliomas. The patients were divided into the training set (n = 72) and test set (n = 32). Apparent diffusion coefficient (ADC), solid tumor distributed diffusion coefficient (DDC), and whole tumor α values were measured. These parameters were applied as cut-off values to determine the predictive accuracy. Proliferating cell nuclear antigen and Ki-67 expression correlated with all parameters.

RESULTS

Significant differences between low-grade gliomas (LGG) and high-grade gliomas (HGG) were observed for all parameters (P < 0.05), and significant differences in the ability of DDC to distinguish between any two glioma grades (P < 0.05) were also evident. DDC showed the highest sensitivity and specificity for glioma grading and was negatively correlated with Ki-67 and proliferating cell nuclear antigen expression. DDC also showed greater predictive accuracy than ADC and α.

CONCLUSION

SEM-DWI offers a better approach for glioma grading than MEM-DWI, and DDC may be a better imaging biomarker for grading and evaluating the proliferative activity of brain gliomas.

Characterization of an Orthotopic Rat Model of Glioblastoma Using Multiparametric Magnetic Resonance Imaging and Bioluminescence Imaging

Glioblastoma multiforme (GBM) is the most common primary brain tumor, with most patients dying within 15–18 months of diagnosis despite aggressive therapy. Preclinical GBM models are valuable for exploring GBM progression and for evaluating new therapeutics or imaging approaches. The rat C6 glioma model shares similarities with human GBM, and application of noninvasive imaging enables better study of disease progression. Here, multiparametric magnetic resonance imaging (mpMRI) and bioluminescence imaging (BLI) were applied to characterize longitudinal development of orthotopic luciferase-expressing C6 tumors. Across all rats (n = 11), a large variability was seen for BLI signal, a relative measure of C6 cell viability, but in most individuals, BLI signal peaked at day 11 and decreased thereafter. T2 and contrast-enhanced T1 tumor volumes significantly increased over time (P < .05), and volume measures did not correlate with BLI signal. After day 11, tumor regions of noncontrast enhancement appeared in postcontrast T1-weighted magnetic resonance imaging, and had significantly higher apparent diffusion coefficient values compared with contrast-enhanced regions (P < .05). This suggests formation of ill-perfused, necrotic regions beyond day 11, which were apparent at end-point–matched tissue sections. Our study represents the first combined use of BLI and mpMRI to characterize the progression of disease in the orthotopic C6 rat model, and it highlights the variability in tumor growth, the complementary information from BLI and mpMRI, and the value of multimodality imaging to better characterize tumor development. Future application of these imaging tools will be useful for evaluation of treatment response, and should be pertinent for other preclinical models.

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).

Three-Dimensional Printed Modeling of Diffuse Low-Grade Gliomas and Associated White Matter Tract Anatomy

BACKGROUND

Diffuse low-grade gliomas (DLGGs) represent several pathological entities that infiltrate and invade cortical and subcortical structures in the brain.

OBJECTIVE

To describe methods for rapid prototyping of DLGGs and surgically relevant anatomy.

METHODS

Using high-definition imaging data and rapid prototyping technologies, we were able to generate 3 patient DLGGs to scale and represent the associated white matter tracts in 3 dimensions using advanced diffusion tensor imaging techniques.

RESULTS

This report represents a novel application of 3-dimensional (3-D) printing in neurosurgery and a means to model individualized tumors in 3-D space with respect to subcortical white matter tract anatomy. Faculty and resident evaluations of this technology were favorable at our institution.

CONCLUSION

Developing an understanding of the anatomic relationships existing within individuals is fundamental to successful neurosurgical therapy. Imaging-based rapid prototyping may improve on our ability to plan for and treat complex neuro-oncologic pathology.

Diagnostic accuracy of proton magnetic resonance spectroscopy and perfusion-weighted imaging in brain gliomas follow-up: a single institutional experience

Objectives The objective of this study was to evaluate whether proton magnetic resonance spectroscopy and perfusion magnetic resonance imaging (MRI) are able to increase diagnostic accuracy in the follow-up of brain gliomas, identifying the progression of disease before it becomes evident in the standard MRI; also to evaluate which of the two techniques has the best diagnostic accuracy. Methods Eighty-three patients with cerebral glioma (50 high-grade gliomas (HGGs), 33 low-grade gliomas (LGGs)) were retrospectively enrolled. All patients underwent standard MRI, H spectroscopic and perfusion echo-planar imaging MRI. For spectroscopy variations of choline/creatine, choline/N-acetyl-aspartate ratio, and lipids and lactates peak were considered. For perfusion 2.0 was considered the cerebral blood volume cut-off for progression. The combination of functional parameters gave a multiparametric score (0-2) to predict outcome. Diagnostic performance was determined by the receiver operating characteristic curve, with sensitivity, specificity, positive predictive and negative predictive values. Results In patients with LGGs a combined score of at least 1 was the best predictor for progression (odds ratio (OR) 3.91) with 8.4 months median anticipation of diagnosis compared to standard MRI. The individual advanced magnetic resonance technique did not show a diagnostic accuracy comparable to the combination of the two. Overall diagnostic accuracy area under the curve (AUC) was 0.881. In patients with HGGs the multiparametric score did not improve diagnostic accuracy significantly. Perfusion MRI was the best predictor of progression (OR 3.65), with 6.7 months median anticipation of diagnosis. Overall diagnostic accuracy AUC was 0.897. Then spectroscopy and perfusion MRI are able to identify tumour progression during follow-up earlier than standard MRI. Conclusion In patients with LGGs the combination of the functional parameters seems to be the best method for diagnosis of progression. In patients with HGGs perfusion is the best diagnostic method.

Nonenhancing peritumoral hyperintense lesion on diffusion-weighted imaging in glioblastoma: a novel diagnostic and specific prognostic indicator

OBJECTIVE Glioblastoma differentials include intracranial tumors, like malignant lymphomas and metastatic brain tumors with indiscernible radiological characteristics. The purpose of this study was to identify a distinct radiological feature for the preoperative differentiation of glioblastoma from its differentials, which include malignant lymphomas and metastatic brain tumors. METHODS Preoperative MR images, including diffusion-weighted imaging (DWI) studies (b = 1000 and 4000 sec/mm²), obtained in patients with newly diagnosed malignant tumor, were analyzed retrospectively after receiving approval from the institutional review board. Sixty-four patients with histologically confirmed glioblastoma, 32 patients with malignant lymphoma, and 46 patients with brain metastases were included. The presence of a nonenhancing peritumoral DWI high lesion (NePDHL, i.e., hyperintense lesion in a nonenhancing peritumoral area on DWI) was confirmed in both DWI sequences. Gray matter lesions were excluded. Lesions were termed "definite" if present within 3 cm of the hyperintense tumor border with a signal intensity ratio ≥ 30% when compared with the contralateral normal white matter in both sequences. Discriminant analysis between the histological diagnosis and the presence of Definite-NePDHL was performed, as well as Kaplan-Meier survival analysis incorporating the existence of Definite-NePDHL. RESULTS In 25% of glioblastoma patients, Definite-NePDHL was present, while it was conspicuously absent in patients with malignant lymphoma and metastatic brain tumors. The specificity and positive predictive value were 100%. In the glioblastoma subset, a higher preoperative Karnofsky Performance Scale score (p = 0.0028), high recursive partitioning analysis class (p = 0.0006), and total surgical removal (p = 0.0012) were associated with better median overall survival. Patients with Definite-NePDHL had significantly early local (p = 0.0467) and distant/dissemination recurrence (p < 0.0001) and poor prognosis (p = 0.0007). CONCLUSIONS The presence of Definite-NePDHL is very specific for glioblastoma and indicates poor prognosis. Definite-NePDHL is a significant indicator of early local and distant/dissemination recurrence in patients with glioblastoma. Studying peritumoral DWI and high-b-value DWI is useful for tumor differentiation.

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

Alexander Disease

Alexander disease is a leukodystrophy caused by dominant missense mutations in the gene encoding the glial fibrillary acidic protein. Individuals with this disorder often present with a typical neuroradiologic pattern including white matter abnormalities with brainstem involvement, selective contrast enhancement, and structural changes to the basal ganglia/thalamus. In rare cases, focal lesions have been seen and cause concern for primary malignancies. Here the authors present an infant initially diagnosed with a chiasmatic astrocytoma that was later identified as having glial fibrillary acidic protein mutation-confirmed Alexander disease. Pathologic and radiologic considerations that were helpful in arriving at the correct diagnosis are discussed.

Proton MR Spectroscopy and Diffusion MR Imaging Monitoring to Predict Tumor Response to Interstitial Photodynamic Therapy for Glioblastoma

Despite recent progress in conventional therapeutic approaches, the vast majority of glioblastoma recur locally, indicating that a more aggressive local therapy is required. Interstitial photodynamic therapy (iPDT) appears as a very promising and complementary approach to conventional therapies. However, an optimal fractionation scheme for iPDT remains the indispensable requirement. To achieve that major goal, we suggested following iPDT tumor response by a non-invasive imaging monitoring. Nude rats bearing intracranial glioblastoma U87MG xenografts were treated by iPDT, just after intravenous injection of AGuIX® nanoparticles, encapsulating PDT and imaging agents. Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) allowed us an original longitudinal follow-up of post-treatment effects to discriminate early predictive markers. We successfully used conventional MRI, T2 star (T2*), Diffusion Weighted Imaging (DWI) and MRS to extract relevant profiles on tissue cytoarchitectural alterations, local vascular disruption and metabolic information on brain tumor biology, achieving earlier assessment of tumor response. From one day post-iPDT, DWI and MRS allowed us to identify promising markers such as the Apparent Diffusion Coefficient (ADC) values, lipids, choline and myoInositol levels that led us to distinguish iPDT responders from non-responders. All these responses give us warning signs well before the tumor escapes and that the growth would be appreciated.

Advanced neuroimaging in the clinic: critical appraisal of the evidence base

The shortage of high-quality systematic reviews in the field of radiology limits evidence-based integration of imaging methods into clinical practice and may perpetuate misconceptions regarding the efficacy and appropriateness of imaging techniques for specific applications. Diffusion tensor imaging for patients with mild traumatic brain injury (DTI-mTBI) and dynamic susceptibility contrast MRI for patients with glioma (DSC-glioma) are applications of quantitative neuroimaging, which similarly detect manifestations of disease where conventional neuroimaging techniques cannot. We performed a critical appraisal of reviews, based on the current evidence-based medicine methodology, addressing the ability of DTI-mTBI and DSC-glioma to (a) detect brain abnormalities and/or (b) predict clinical outcomes. 23 reviews of DTI-mTBI and 26 reviews of DSC-glioma met criteria for inclusion. All reviews addressed detection of brain abnormalities, whereas 12 DTI-mTBI reviews and 22 DSC-glioma reviews addressed prediction of a clinical outcome. All reviews were assessed using a critical appraisal worksheet consisting of 19 yes/no questions. Reviews were graded according to the total number of positive responses and the 2011 Oxford Centre for evidence-based medicine levels of evidence criteria. Reviews addressing DTI-mTBI detection had moderate quality, while those addressing DSC-glioma were of low quality. Reviews addressing prediction of outcomes for both applications were of low quality. Five DTI-mTBI reviews, but only one review of DSC-glioma met criteria for classification as a meta-analysis/systematic/quantitative review.

Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: Single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging

The accurate identification of glioblastoma progression remains an unmet clinical need. The aim of this prospective single-institutional study is to determine and validate thresholds for the main metabolite concentrations obtained by MR spectroscopy (MRS) and the values of the apparent diffusion coefficient (ADC) to enable distinguishing tumor recurrence from pseudoprogression. Thirty-nine patients after the standard treatment of a glioblastoma underwent advanced imaging by MRS and ADC at the time of suspected recurrence — median time to progression was 6.7 months. The highest significant sensitivity and specificity to call the glioblastoma recurrence was observed for the total choline (tCho) to total N-acetylaspartate (tNAA) concentration ratio with the threshold ≥ 1.3 (sensitivity 100.0% and specificity 94.7%). The ADCmean value higher than 1313 × 10^− 6 mm²/s was associated with the pseudoprogression (sensitivity 98.3%, specificity 100.0%). The combination of MRS focused on the tCho/tNAA concentration ratio and the ADCmean value represents imaging methods applicable to early non-invasive differentiation between a glioblastoma recurrence and a pseudoprogression. However, the institutional definition and validation of thresholds for differential diagnostics is needed for the elimination of setup errors before implementation of these multimodal imaging techniques into clinical practice, as well as into clinical trials.

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For an effective salvage treatment, an accurate diagnosis of GBM recurrence is essential.

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The standard structural MRI has limited sensitivity and specificity to distinguish GBM progression.

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GBM recurrence is characterized by the ADCmean value ≤ 1313 × 10^− 6 mm²/s and the tCho/tNAA ratio ≥ 1.3.

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An institutional definition of thresholds is needed, if advanced imaging should be used accurately in clinical practice.

The Diagnostic Ability of Follow-Up Imaging Biomarkers after Treatment of Glioblastoma in the Temozolomide Era: Implications from Proton MR Spectroscopy and Apparent Diffusion Coefficient Mapping

Objective. To prospectively determine institutional cut-off values of apparent diffusion coefficients (ADCs) and concentration of tissue metabolites measured by MR spectroscopy (MRS) for early differentiation between glioblastoma (GBM) relapse and treatment-related changes after standard treatment. Materials and Methods. Twenty-four GBM patients who received gross total resection and standard adjuvant therapy underwent MRI examination focusing on the enhancing region suspected of tumor recurrence. ADC maps, concentrations of N-acetylaspartate, choline, creatine, lipids, and lactate, and metabolite ratios were determined. Final diagnosis as determined by biopsy or follow-up imaging was correlated to the results of advanced MRI findings. Results. Eighteen (75%) and 6 (25%) patients developed tumor recurrence and pseudoprogression, respectively. Mean time to radiographic progression from the end of chemoradiotherapy was 5.8 ± 5.6 months. Significant differences in ADC and MRS data were observed between those with progression and pseudoprogression. Recurrence was characterized by N-acetylaspartate ≤ 1.5 mM, choline/N-acetylaspartate ≥ 1.4 (sensitivity 100%, specificity 91.7%), N-acetylaspartate/creatine ≤ 0.7, and ADC ≤ 1300 × 10⁻⁶ mm²/s (sensitivity 100%, specificity 100%). Conclusion. Institutional validation of cut-off values obtained from advanced MRI methods is warranted not only for diagnosis of GBM recurrence, but also as enrollment criteria in salvage clinical trials and for reporting of outcomes of initial treatment.

Inhibition of Pediatric Glioblastoma Tumor Growth by the Anti-Cancer Agent OKN-007 in Orthotopic Mouse Xenografts

Pediatric glioblastomas (pGBM), although rare, are one of the leading causes of cancer-related deaths in children, with tumors essentially refractory to existing treatments. Here, we describe the use of conventional and advanced in vivo magnetic resonance imaging (MRI) techniques to assess a novel orthotopic xenograft pGBM mouse (IC-3752GBM patient-derived culture) model, and to monitor the effects of the anti-cancer agent OKN-007 as an inhibitor of pGBM tumor growth. Immunohistochemistry support data is also presented for cell proliferation and tumor growth signaling. OKN-007 was found to significantly decrease tumor volumes (p<0.05) and increase animal survival (p<0.05) in all OKN-007-treated mice compared to untreated animals. In a responsive cohort of treated animals, OKN-007 was able to significantly decrease tumor volumes (p<0.0001), increase survival (p<0.001), and increase diffusion (p<0.01) and perfusion rates (p<0.05). OKN-007 also significantly reduced lipid tumor metabolism in responsive animals [(Lip1.3 and Lip0.9)-to-creatine ratio (p<0.05)], as well as significantly decrease tumor cell proliferation (p<0.05) and microvessel density (p<0.05). Furthermore, in relationship to the PDGFRα pathway, OKN-007 was able to significantly decrease SULF2 (p<0.05) and PDGFR-α (platelet-derived growth factor receptor-α) (p<0.05) immunoexpression, and significantly increase decorin expression (p<0.05) in responsive mice. This study indicates that OKN-007 may be an effective anti-cancer agent for some patients with pGBMs by inhibiting cell proliferation and angiogenesis, possibly via the PDGFRα pathway, and could be considered as an additional therapy for pediatric brain tumor patients.

Near-infrared fluorescence heptamethine carbocyanine dyes mediate imaging and targeted drug delivery for human brain tumor

Brain tumors and brain metastases are among the deadliest malignancies of all human cancers, largely due to the cellular blood-brain and blood-tumor barriers that limit the delivery of imaging and therapeutic agents from the systemic circulation to tumors. Thus, improved strategies for brain tumor visualization and targeted treatment are critically needed. Here we identified and synthesized a group of near-infrared fluorescence (NIRF) heptamethine carbocyanine dyes and derivative NIRF dye-drug conjugates for effective imaging and therapeutic targeting of brain tumors of either primary or metastatic origin in mice, which is mechanistically mediated by tumor hypoxia and organic anion-transporting polypeptide genes. We also demonstrate that these dyes, when conjugated to chemotherapeutic agents such as gemcitabine, significantly restricted the growth of both intracranial glioma xenografts and prostate tumor brain metastases and prolonged survival in mice. These results show promise in the application of NIRF dyes as novel theranostic agents for the detection and treatment of brain tumors.

Canine brain tumours: a model for the human disease?

Canine brain tumours are becoming established as naturally occurring models of disease to advance diagnostic and therapeutic understanding successfully. The size and structure of the dog's brain, histopathology and molecular characteristics of canine brain tumours, as well as the presence of an intact immune system, all support the potential success of this model. The limited success of current therapeutic regimens such as surgery and radiation for dogs with intracranial tumours means that there can be tremendous mutual benefit from collaboration with our human counterparts resulting in the development of new treatments. The similarities and differences between the canine and human diseases are described in this article, emphasizing both the importance and limitations of canines in brain tumour research. Recent clinical veterinary therapeutic trials are also described to demonstrate the areas of research in which canines have already been utilized and to highlight the important potential benefits of translational research to companion dogs.

OKN-007 decreases tumor necrosis and tumor cell proliferation and increases apoptosis in a preclinical F98 rat glioma model

BACKGROUND

Glioblastoma is a malignant World Health Organization (WHO) grade IV glioma with a poor prognosis in humans. New therapeutics are desperately required. The nitrone OKN-007 (2,4-disulfophenyl-PBN) has demonstrated effective anti-glioma properties in several rodent models and is currently being used as a clinical investigational drug for recurrent gliomas. We assessed the regional effects of OKN-007 in the tumor necrotic core and non-necrotic tumor parenchyma.

METHODS

An F98 rat glioma model was evaluated using proton magnetic resonance spectroscopy ((1) H-MRS), diffusion-weighted imaging (DWI), morphological T2-weighted imaging (T2W) at 7 Tesla (30 cm-bore MRI), as well as immunohistochemistry and microarray assessments, at maximum tumor volumes (15-23 days following cell implantation in untreated (UT) tumors, and 18-35 days in OKN-007-treated tumors).

RESULTS

(1) H-MRS data indicates that Lip0.9/Cho, Lip0.9/Cr, Lip1.3/Cho, and Lip1.3/Cr ratios are significantly decreased (all P < 0.05) in the OKN-007-treated group compared with UT F98 gliomas. The Cho/Cr ratio is also significantly decreased in the OKN-007-treated group compared with UT gliomas. In addition, the OKN-007-treated group demonstrates significantly lower ADC values in the necrotic tumor core and the nonnecrotic tumor parenchyma (both P < 0.05) compared with the UT group. There was also an increase in apoptosis following OKN-007 treatment (P < 0.01) compared with UT.

CONCLUSION

OKN-007 reduces both necrosis and tumor cell proliferation, as well as seems to mediate multiple effects in different tumor regions (tumor necrotic core and nonnecrotic tumor parenchyma) in F98 gliomas, indicating the efficacy of OKN-007 as an anti-cancer agent and its potential clinical use.

Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading

The preoperative grading of gliomas, which is critical for guiding therapeutic strategies, remains unsatisfactory. We aimed to retrospectively assess the efficacy of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in the grading of gliomas. Forty-two newly diagnosed glioma patients underwent conventional MR imaging, DWI, and contrast-enhanced MR imaging. Parameters of apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast ADC (f) were generated. They were tested for differences between low- and high-grade gliomas based on one-way ANOVA. Receiver-operating characteristic (ROC) analyses were conducted to determine the optimal thresholds as well as the sensitivity and specificity for grading. ADC, D, and f were higher in the low-grade gliomas, whereas D* tended to be lower (all P<0.05). The AUC, sensitivity, specificity and the cutoff value, respectively, for differentiating low- from high-grade gliomas for ADC, D and f, and differentiating high- from low-grade gliomas for D* were as follows: ADC, 0.926, 100%, 82.8%, and 0.7 × 10⁻³ mm²/sec; D, 0.942, 92.3%, 86.2%, and 0.623 × 10⁻³ mm²/sec; f, 0.902, 92.3%, 86.2%, and 35.3%; D*, 0.798, 79.3%, 84.6%, and 0.303 × 10⁻³ mm²/sec. The IVIM DWI demonstrates efficacy in differentiating the low- from high-grade gliomas.

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.

Imaging of brain tumors

Neuroimaging plays a crucial role in diagnosis of brain tumors and in the decision-making process for therapy. Functional imaging techniques can reflect cellular density (diffusion imaging), capillary density (perfusion techniques), and tissue biochemistry (magnetic resonance [MR] spectroscopy). In addition, cortical activation imaging (functional MR imaging) can identify various loci of eloquent cerebral cortical function. Combining these new tools can increase diagnostic specificity and confidence. Familiarity with conventional and advanced imaging findings facilitates accurate diagnosis, differentiation from other processes, and optimal patient treatment. This article is a practical synopsis of pathologic, clinical, and imaging spectra of most common brain tumors.

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.

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We have developed a novel unsupervised method for voxel-based clustered imaging.

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Each class ratio in clustered images differentiated high from low-grade gliomas.

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The 16-class clustered images showed the best performance for the differentiation.

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Each class comprised different patterns of the seven diffusion tensor-based features.

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Multiple parameters from diffusion tensor images are useful for glioma grading.