Differentiation of Primary Central Nervous System Lymphoma From Glioblastoma: Quantitative Analysis Using Arterial Spin Labeling and Diffusion Tensor Imaging

Utility of diffusion tensor imaging in differentiating benign from malignant thyroid nodules

Purpose: To assess diffusion tensor imaging (DTI) in differentiating benign from malignant thyroid nodules. Methods: A retrospective analysis was done on 55 patients with thyroid nodules who had undergone DTI. The fraction anisotropy (FA) and mean diffusivity (MD) of the thyroid nodules were measured using region of interest (ROI) by two observers. The final diagnosis was malignant and benign, as proved by pathological examination. Results: The mean MD of benign thyroid nodules (1.84 ± 0.42 and 1.90 ± 0.37 × 10-3mm2/s) was significantly higher (p < .001) than malignant nodules (0.95 ± 0.46 and 0.97 ± 0.41 × 10-3mm2/s) as scored by both observers. The cut-off values of 1.45 and 1.50 × 10-3mm2/s were used to differentiate malignant from benign thyroid nodules with the areas under the curve (AUC) of 0.926 and 0.937, respectively. The mean FA of benign thyroid nodules (0.23 ± 0.07 and 0.24 ± 0.08) was significantly lower (p < .001) than malignant nodules (0.48 ± 0.21 and 0.49 ± 0.18). The FA cut-off value of ≤0.32 and 0.33 was used for differentiating malignant from benign thyroid nodules with an AUC of 0.877 and 0.881, respectively. A combination of MD and FA values was used to differentiate benign from malignant thyroid nodules with an AUC of 0.932 and an accuracy of 87%. There was an excellent agreement between both observers for FA and MD (K = 0.939, 0.929). Conclusion: The DTI is a non-invasive, non-contrast imaging tool that can differentiate benign from malignant thyroid nodules.

Primary central nervous system lymphoma: A mirror type presentation in an immunocompetent patient

Background

Primary central nervous system (CNS) lymphoma is a very rare extranodal non-Hodgkin lymphoma. The bilateral pattern, as we call it "mirror type", has been identified in other CNS lesions such as gliomas, metastases, and demyelinating lesions, so the differential diagnosis includes imaging studies such as magnetic resonance imaging contrasted with spectroscopy, ruling out immunodeficiency or metastatic disease.

Case Description

A 65-year-old female presented progressing headache, loss of memory and language alterations, as well as sensory alterations. Neuroimaging showed the presence of two equidistant periventricular lesions at the level of both ventricular atria, a spectroscopy study suggestive of malignancy. Serological studies showed no evidence of immunodeficiency or the presence of positive tumor markers; however, a biopsy was performed, which revealed a histopathological result of primary lymphoma of the CNS.

Conclusion

In neuro-oncology, primary CNS tumors with multiple lesions are rare, even more, the "mirror type" lesions. Lymphomas are lesions that can present in different ways on imaging and clinical presentation. These tumors that present a vector effect due to their size, perilesional edema, or that lead to loss of neurological function are highly discussed in diagnostic and surgical treatment. Due to their prognosis, action on diagnosis and treatment must be taken as quickly as hospital resources allow.

Advanced diffusion imaging reveals microstructural characteristics of primary CNS lymphoma, allowing differentiation from glioblastoma

Background

Primary CNS lymphoma (PCNSL) and glioblastoma (GBM) both represent frequent intracranial malignancies with differing clinical management. However, distinguishing PCNSL from GBM with conventional MRI can be challenging when atypical imaging features are present. We employed advanced dMRI for noninvasive characterization of the microstructure of PCNSL and differentiation from GBM as the most frequent primary brain malignancy.

Methods

Multiple dMRI metrics including Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging, and Diffusion Microstructure Imaging were extracted from the contrast-enhancing tumor component in 10 PCNSL and 10 age-matched GBM on 3T MRI. Imaging findings were correlated with cell density and axonal markers obtained from histopathology.

Results

We found significantly increased intra-axonal volume fractions (V-intra and intracellular volume fraction) and microFA in PCNSL compared to GBM (all P < .001). In contrast, mean diffusivity (MD), axial diffusivity (aD), and microADC (all P < .001), and also free water fractions (V-CSF and V-ISO) were significantly lower in PCNSL (all P < .01). Receiver-operating characteristic analysis revealed high predictive values regarding the presence of a PCNSL for MD, aD, microADC, V-intra, ICVF, microFA, V-CSF, and V-ISO (area under the curve [AUC] in all >0.840, highest for MD and ICVF with an AUC of 0.960). Comparative histopathology between PCNSL and GBM revealed a significantly increased cell density in PCNSL and the presence of axonal remnants in a higher proportion of samples.

Conclusions

Advanced diffusion imaging enables the characterization of the microstructure of PCNSL and reliably distinguishes PCNSL from GBM. Both imaging and histopathology revealed a relatively increased cell density and a preserved axonal microstructure in PCNSL.

Advances in the Radiological Evaluation of and Theranostics for Glioblastoma

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Multi-parametric arterial spin labeling and diffusion-weighted imaging of paranasal sinuses masses

PURPOSE

To evaluate arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) in discrimination of benign from malignant paranasal sinus (PNS) tumors.

MATERIAL AND METHODS

A prospective study was done upon 42 cases of PNS masses that underwent magnetic resonance ASL and DWI of the head. Tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the masses were calculated by two observers. The pathological diagnosis was malignant (n = 28) and benign (n = 14) cases.

RESULTS

For both observers, the malignant PNS masses had significantly higher TBF (P < 0.001, 0.001) and lower ADC (P < 0.001, 0.001) than in benign masses. The ROC curve analysis of TBF, The threshed TBF was (121.45, 122.68 mL/100 g/min) used for differentiation between benign and malignant PNS masses, revealed sensitivity (92.9%, 89.3%), specificity (85.7%, 85.7%), accuracy (90.5%, 88.1%) and the AUC was 0.87 and 0.86 by both observers. the ROC curve analysis of ADC, The threshold ADC (1.215, 1.205 X10^-3mm2/s) was used for differentiation between benign and malignant PNS masses, revealed sensitivity (96.4%, 89.3%), specificity (78.6%, 78.6%), accuracy of (90.5%, 85.7%) and the AUC was 0.93 and 0.92 by both observers. Combined analysis of TBF and ADC used for differentiation between benign and malignant PNS masses had revealed sensitivity (96.4%, 89.3%), specificity (92.9%, 85.7%) accuracy of (95.2%, 88.1%) and AUC. (0.995, 0.985) for both observers.

CONCLUSION

Combined using of TBF and ADC have a role in differentiation malignant from benign PNS masses.

Diffusion tensor imaging of dorsal stream language areas in patients with post-stroke aphasia

Background

Aphasia complicating stroke occurs due to language deficits that decrease communication abilities and functional independence. Our study aims to assess fractional anisotropy (FA) and mean diffusivity (MD) parameters of diffusion tensor imaging (DTI) of the dorsal stream language areas in patients with post-stroke aphasia. It was conducted on 27 patients with post-stroke aphasia and 27 age- and sex-matched controls who underwent DTI of the brain. FA and MD values of Broca's area (BA), Wernick's area (WA), superior longitudinal fasciculus (SLF), and arcuate fasciculus (AF), and number of tract fibers (TF) of AF and SLF were calculated. Results were correlated with National Institutes of Health Stroke Scale (NIHSS), Arabic version of Comprehensive Aphasia Test (Arabic CAT), and Mansoura Arabic Screening Aphasia Test (MASAT).

Results

FA of AF and SLF in patients was significantly lower (P = 0.001) than controls. MD of AF and SLF in patients was significantly higher (P = 0.001) than controls. The mean volume TF of AF and SLF in patients was significantly (P = 0.001) lower than the mean volume in controls for AF and SLF. FA cutoff for AF was 0.34 and for SLF, it was 0.35 with sensitivity, specificity, and accuracy (85.2%, 62.1%, 73.2%) for AF, (74.1%, 69%, 71.4%) for SLF, respectively. MD cutoff value for AF was 0.87, and 0.84 for SLF with sensitivity, specificity, and accuracy (63%, 72.4%, 67.8%) for AF, (81.5%, 79.3%, 80.4%) for SLF, respectively. Cutoff TF of AF was 1728 and for SLF it was 601 with sensitivity, specificity, and accuracy (88.9%, 72.4%, 80.4%) for AF and (85.2%, 85.2%, 78.6%) for SLF, respectively.

Conclusions

DTI is a non-invasive promising method that can be used to assess language areas in patients with post-stroke aphasia.

Primary CNS lymphoma in Immunocompetent patients: Appearances on Conventional and Advanced Imaging with Review of literature

Primary central nervous system lymphoma (PCNSL) constitutes about 3% of all primary brain tumors and nearly 1 to 3% of all Non Hodgkin Lymphomas. In the recent years the incidence of primary CNS lymphoma is increasing in immunocompetent patients. As PCNSL are chemosensitive as well as radiosensitive, its early and accurate diagnosis is imperative for optimal management. Contrast enhanced Magnetic Resonance Imaging (MRI) is the recommended imaging modality for PCNSL; however, contrast enhanced Computed Tomography (CE-CT) is done in cases where MRI is contraindicated. Advanced imaging techniques like DWI (diffusion weighted imaging), MRS (MR Spectroscopy), MR perfusion, DTI (Diffusion tensor imaging) are important in diagnosis and help in its differentiation from other tumors.

Diagnostic accuracy of arterial spin labeling in differentiating between primary central nervous system lymphoma and high-grade glioma: a systematic review and meta-analysis

BACKGROUND

Existing studies have confirmed the accuracy of arterial spin labeling (ASL) in differentiating between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG). We aimed to consolidate the existing evidence with a meta-analysis.

METHODS

Six literature databases were searched for relevant papers. After assessing the quality of studies, bivariate regression was performed, and the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic score, diagnostic odds ratio (DOR), and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were calculated, along with the corresponding 95% confidence intervals (CIs). Deeks' test was used to determine risk of publication bias.

RESULTS

Ten high-quality studies, comprising 151 patients with PCNSL and 455 with HGG, were included. The pooled SEN was 0.79 (95% CI: 0.72-0.85), pooled SPE was 0.90 (95% CI: 0.84-0.94), pooled PLR was 8.07 (95% CI: 5.01-13.02), pooled NLR was 0.23 (95% CI: 0.17-0.32), pooled diagnostic score was 3.56 (95% CI: 2.94-4.18), and pooled DOR was 35.10 (95% CI: 18.83-65.45). The AUC of SROC was 0.86 (95% CI: 0.83-0.89). No publication bias was found.

CONCLUSIONS

ASL demonstrated high diagnostic accuracy in differentiating between PCNSL and HGG.

Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study

Objectives

To investigate the value of morphological feature and signal intensity ratio (SIR) derived from conventional magnetic resonance imaging (MRI) in distinguishing primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (aGBM).

Methods

Pathology-confirmed PCNSLs (n = 93) or aGBMs (n = 48) from three institutions were retrospectively enrolled and divided into training cohort (n = 98) and test cohort (n = 43). Morphological features and SIRs were compared between PCNSL and aGBM. Using linear discriminant analysis, multiple models were constructed with SIRs and morphological features alone or jointly, and the diagnostic performances were evaluated via receiver operating characteristic (ROC) analysis. Areas under the curves (AUCs) and accuracies (ACCs) of the models were compared with the radiologists’ assessment.

Results

Incision sign, T₂ pseudonecrosis sign, reef sign and peritumoral leukomalacia sign were associated with PCNSL (training and overall cohorts, P < 0.05). Increased T₁ ratio, decreased T₂ ratio and T₂/T₁ ratio were predictive of PCNSL (all P < 0.05). ROC analysis showed that combination of morphological features and SIRs achieved the best diagnostic performance for differentiation of PCNSL and aGBM with AUC/ACC of 0.899/0.929 for the training cohort, AUC/ACC of 0.794/0.837 for the test cohort and AUC/ACC of 0.869/0.901 for the overall cohort, respectively. Based on the overall cohort, two radiologists could distinguish PCNSL from aGBM with AUC/ACC of 0.732/0.724 for radiologist A and AUC/ACC of 0.811/0.829 for radiologist B.

Conclusion

MRI morphological features can help differentiate PCNSL from aGBM. When combined with SIRs, the diagnostic performance was better than that of radiologists’ assessment.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading

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LRD medical image fusion algorithm can be used for glioma grading.

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We can use the LRD fusion algorithm with MRI image for glioma grading.

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Fusing of DWI (b50) and T₁ enhancement (T₁Gd) by LRD, have highest diagnostic value for glioma grading.

Background

Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.

Procedures

In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.

Findings

We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.

Conclusions

This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

The role of diffusion tensor imaging of the liver in children with autoimmune hepatitis

Purpose

To evaluate the role of diffusion tensor imaging (DTI) of the liver in children with autoimmune hepatitis (AIH).

Material and methods

A prospective study was done on 42 children with AIH (30 girls and 12 boys, with a mean age of 13 years) and 20 age- and sex-matched healthy control children. They underwent DTI of the liver and laboratory tests. Liver biopsy was done for the patients. The mean diffusivity (MD) and fractional anisotropy (FA) of the liver were calculated and correlated with the pathological results.

Results

The mean MD and FA of the liver in children with AIH were 1.42 ± 0.06 × 10^-3 mm²/s and 0.37 ± 0.11; and in the control children they were 1.55 ± 0.07 × 10^-3 mm²/s and 0.25 ± 0.03, respectively. The MD and FA were significantly different in the children with AIH compared to the control children (p = 0.001). The cutoff MD and FA used to differentiate patients from controls were 1.50 × 10^-3 mm²/s, 0.31 with AUC of 0.919 and 0.813, sensitivity of 97.6% and 66.7%, a specificity of 80% and 70%, an accuracy of 94.2% and 67.3%, PPV of 95.3 and 90.3, and NPV of 88.9 and 33.3, respectively. There was significantly lower MD and higher FA of the liver in children with AIH type I (n = 31) than type II (n = 11) (p = 0.001), and patients with (n = 9) and without (n = 33) overlap syndrome (p = 0.005).

Conclusions

We concluded that DTI parameters can help to diagnose AIH, detect its phenotyping, and give clues as to the presence of associated overlap syndrome.

Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain

Objectives

Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors.

Methods

We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve.

Results

We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10^-3 mm²/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10^-3 mm²/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors.

Conclusions

The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.

The value of asphericity derived from T1-weighted MR in differentiating intraparenchymal ring-enhancing lesions-comparison of glioblastomas and brain abscesses

BACKGROUND

Both brain abscess(BA)and glioblastoma (GBM) are common causative pathologies of intraparenchymal ring-enhancing lesions. Advanced MR sequences such as diffusion weighted image (DWI) were often used to increase distinguishability of both entities.

PURPOSE

To evaluate the value of asphericity (ASP) from conventional T1-weighted MR images in differentiating BA from morphologically similar ring-enhancing GBM.

MATERIAL AND METHODS

Twenty-one BA and twenty-nine GBM were retrospectively included in this study. Each region of interest (ROI) was delineated twice with the software of ITK-SNAP on the contrast-enhanced T1 images by two observers. ASP was calculated to define the relative deviation of the ROI's shape from a sphere. Intraclass correlation coefficients (ICC) for inter-observer and intra-observer were calculated. The diagnostic capabilities of ASP and conventional volume (VOL) of ROI were evaluated with receiver operating characteristic (ROC) curve analysis. In addition, areas under the ROC curves of ASP and VOL were compared.

RESULTS

ICC of intra-observer and inter-observer were 0.99 (95% confidence interval, [CI] 0.97-0.99) and 0.98 (0.95-0.99), respectively. Both ASP and VOL showed significant difference between BA and GBM. The mean ASP values for BA and GBM were 66.3±7.8 and 14.7±1.8, respectively. The mean VOL value of BA was also larger than that of GBM (47.2±7.4 vs. 20.7±1.5 mm³). The mean AUC of ASP and VOL were 0.977 (95% CI 0.944-1) and 0.86 (95% CI 0.746-0.974), respectively. The AUC of ASP was significantly higher than that of VOL (p=0.04). The optimal cut point values of ASP and VOL were 24.39 and 24.86 mm³, respectively.

CONCLUSIONS

ASP derived from routine MRI is useful in differentiating BA from GBM.

Diffusion Tensor Imaging of Brain Metastases in Patients with Breast Cancer According to Molecular Subtypes

BACKGROUND AND PURPOSE

Recent studies have shown that diffusion tensor imaging (DTI) parameters are used to follow the patients with breast cancer and correlate well as a prognostic parameter of breast cancer. However, as far as we know, there is no data to compare the DTI features of breast cancer brain metastases according to molecular subtypes in the literature. Our aim is to evaluate whether there are any differences in DTI parameters of brain metastases in patients with breast cancer according to molecular subtypes.

METHODS

Twenty-seven patients with breast cancer and 82 metastatic brain lesions were included. We classified subjects into three subgroups according to their hormone expression; Group 0, triple- negative (n; 6, 19 lesions), group 1, HER2-positive (n;16, 54 lesions) and group 2, hormone-- positive group (n; 5, 9 lesions). The apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) values in DTI were measured and compared between three groups.

RESULTS

ADC, AD and RD values of group 2 were significantly lower compared to group 0. No significant differences were found in FA, ADC, AD and RD values between the group 0 and 1 and the group 1 and 2.

CONCLUSION

Metastasis of aggressive triple-negative breast cancer showed higher ADC values compared to the less aggressive hormone-positive group. Higher ADC values in brain metastases of breast cancer may indicate a poor prognosis, so DTI findings could play a role in planning appropriate treatment.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study

OBJECTIVES

To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma.

METHODS

Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve.

RESULTS

The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909-0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice.

CONCLUSIONS

T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

Optical and magnetic resonance imaging approaches for investigating the tumour microenvironment: state-of-the-art review and future trends

The tumour microenvironment (TME) strongly influences tumorigenesis and metastasis. Two of the most characterized properties of the TME are acidosis and hypoxia, both of which are considered hallmarks of tumours as well as critical factors in response to anticancer treatments. Currently, various imaging approaches exist to measure acidosis and hypoxia in the TME, including magnetic resonance imaging (MRI), positron emission tomography and optical imaging. In this review, we will focus on the latest fluorescent-based methods for optical sensing of cell metabolism and MRI as diagnostic imaging tools applied both in vitro and in vivo. The primary emphasis will be on describing the current and future uses of systems that can measure intra- and extra-cellular pH and oxygen changes at high spatial and temporal resolution. In addition, the suitability of these approaches for mapping tumour heterogeneity, and assessing response or failure to therapeutics will also be covered.

Imaging of vascular cognitive impairment

Vascular cognitive impairment (VCI) is a major health challenge and represents the second most common cause of dementia. We review the updated imaging classification and imaging findings of different subtypes of VCI. We will focus on the magnetic resonance imaging (MRI) markers of each subtype and highlight the role of advanced MR imaging sequences in the evaluation of these patients. Small vessel dementia appears as white matter hyperintensity, lacunae, microinfarcts, and microbleeds. Large vessel dementia includes strategic infarction and multi-infarction dementias. Hypoperfusion dementia can be seen as watershed infarcts and cortical laminar necrosis. Hemorrhagic dementia results from cerebral amyloid angiopathy and cortical superficial siderosis. Hereditary forms of VCI, caused by gene mutations such as CADASIL, should be suspected when dementia presents in young patients. Mixed dementia is seen in patients with Alzheimer's disease and the coexistence of cerebrovascular disease.

Brain metastases assessment by FDG-PET/CT: can it eliminate the necessity for dedicated brain imaging?

Background

Brain metastases (BM) are the most common intracranial tumors in adults outnumbering all other intracranial neoplasms. Positron emission tomography combined with computed tomography (PET/CT) is a widely used imaging modality in oncology with a unique combination of cross-sectional anatomic information provided by CT and the metabolic information provided by PET using the [18F]-2-fluoro-2-deoxy-d-glucose (FDG) as a tracer. The aim of the study is to assess the role and diagnostic performance of brain-included whole-body PET/CT in detection and evaluation of BM and when further imaging is considered necessary. The study was conducted over a period of 12 months on 420 patients suffering from extra-cranial malignancies utilizing brain-included whole-body PET/CT.

Results

Thirty patients with 71 brain lesions were detected, 18 patients (60%) had BM of unknown origin while 12 patients (40%) presented with known primary tumors. After brain-included whole-body FDG-PET/CT examination, the unknown primaries turned out to be bronchogenic carcinoma in 10 patients (33.3%), renal cell carcinoma in 2 patients (6.7%), and lymphoma in 2 patients (6.7%), yet the primary tumors remained unknown in 4 patients (13.3%). In 61 lesions (85.9%), the max SUV ranged from 0.2- < 10, while in 10 lesions (14.1%) the max SUV ranged from 10 to 20. Hypometabolic lesions were reported in 41 (57.7%) lesions, hypermetabolic in 3 lesions (4.2%), whereas 27 lesions (38.0%) showed similar FDG uptake to the corresponding contralateral brain matter. PET/CT overall sensitivity, specificity, positive and negative predictive, and accuracy values were 78.1, 92.6, 83.3, 90, and 88% respectively.

Conclusion

Brain-included whole-body FDG-PET/CT provides valuable complementary information in the evaluation of patients with suspected BM. However, the diagnostic performance of brain PET-CT carries the possibility of false-negative results with consequent false sense of security. The clinicians should learn about the possible pitfalls of PET/CT interpretation to direct patients with persistent neurological symptoms or high suspicion for BM for further dedicated CNS imaging.

Arterial spin labeling and diffusion-weighted MR imaging: Utility in differentiating idiopathic orbital inflammatory pseudotumor from orbital lymphoma

PURPOSE

To assess arterial spin-labeling (ASL) and diffusion-weighted imaging (DWI) and in combination for differentiating between idiopathic orbital inflammatory pseudotumor (IOIP) and orbital lymphoma.

MATERIAL AND METHODS

A retrospective study was done on 37 untreated patients with orbital masses, suspected to be IOIP or orbital lymphoma that underwent ASL and DWI of the orbit. Quantitative measurement of tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the orbital lesion was done.

RESULTS

There was a significant difference (P = 0.001) in TBF between patients with IOIP (n = 21) (38.1 ± 6.2, 40.3 ± 7.1 ml/100 g/min) and orbital lymphoma (n = 16) (55.5 ± 7.1, 56.8 ± 7.9 ml/100 g/min) for both observers respectively. Thresholds of TBF used for differentiating IOIP from orbital lymphoma were 48, 46 ml/100 g/min revealed area under the curve (AUC) of (0.958 and 0.921), and accuracy of (86% and 83%) for both observers respectively. There was a significant difference (P = 0.001) in ADC between patients with IOIP (1.04 ± 0.19, 1.12 ± 0.23 × 10^-3 mm²/s) and orbital lymphoma (0.69 ± 0.10, 0.72 ± 0.11 × 10^-3 mm²/s) for both observers respectively. Thresholds of ADC used for differentiating IOIP from orbital lymphoma were 0.84 and 0.86 × 10^-3 mm²/s with AUC of (0.933 and 0.920), and accuracy of 89% and 90% for both observers respectively. The combined TBF and ADC used for differentiating IOIP from orbital lymphoma had AUC of (0.973 and 0.970) and accuracy of (91% and 89%) for both observers respectively.

CONCLUSION

TBF and ADC alone and in combination are useful for differentiating IOIP from orbital lymphoma.

Heterogeneity of Baló's concentric sclerosis: a study of eight cases with different therapeutic concepts

Background

Baló’s Concentric Sclerosis (BCS) is a rare heterogeneous demyelinating disease with a variety of phenotypes on Magnetic Resonance Imaging (MRI). Existing literature lacks data especially on the therapeutic approach of the disease which we intended to elucidate by means of suggesting a new possible BCS classification and introducing different therapeutic concepts based on each BCS-subgroup characteristics.

Methods

We present a retrospective study of eight treated patients with BCS-type lesions, emphasizing on MRI characteristics and differences on therapeutic maneuvers.

Results

Data analysis showed: at disease onset the BCS-type lesion was tumefactive (size ≥2 cm) in 6 patients, with a mean size of 2.7 cm (± 0.80 SD); a coexistence of MS-like plaques on brain MRI was identified in 7 patients of our cohort. The mean age was 26.3 years (±7.3 SD) at disease onset and the mean follow-up period was 56.8 months (range 9–132 months). According to radiological characteristics and response to therapies, we further categorized them into 3 subgroups: a) Group-1; BCS with or without coexisting nonspecific white matter lesions; poor response to intravenous methylprednisolone (IVMP); treated with high doses of immunosuppressive agents (4 patients), b) Group-2; BCS with typical MS lesions; good response to IVMP; treated with MS-disease modifying therapies (2 patients), c) Group-3; BCS with typical MS lesions; poor response to IVMP; treated with rituximab (2 patients).

Conclusions

Our study introduces a new insight regarding the categorization of BCS into three subgroups depending on radiological features at onset and during the course of the disease, in combination with the response to different immunotherapies. Immunosuppressive agents such as cyclophosphamide are usually effective in BCS. However, therapeutic alternatives like anti-CD20 monoclonal antibodies or more classical disease-modifying MS therapies can be considered when BCS has also mixed lesions similar to MS. Future studies with a larger sample size are necessary to further establish these findings, thus leading to better treatment algorithms and improved clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-020-01971-2.

Intracranial Mimics of Cauda Equina Syndrome: Heads or Tails?

BACKGROUND

To report cases of extraspinal mimics of cauda equina syndrome (CES) to enable prompt diagnosis and treatment in the future. CES results from compression of spinal nerve roots. Current practice mandates lumbosacral magnetic resonance imaging (MRI) scanning to diagnose CES. However, it may not reveal compression or provide an explanation for the presentation. We present 3 cases of suspected CES who went on to have intracranial pathology.

METHODS

Retrospective review of all patients presenting with CES-type features who were subsequently found to have causative intracranial pathology over a 6-month period.

RESULTS

Three cases were found, and these are hereby presented.

CASE PRESENTATION

Case 1: A 57-year-old lady presented with urinary retention and bilateral leg weakness. She underwent an MRI spine which showed no evidence of CES. She was diagnosed with haemorrhagic intracranial metastases. Case 2: A 52-year-old lady presented with lower back and right buttock pain, with right-sided leg numbness, saddle hypoesthesia, and bowel and bladder incontinence. MRI spine showed no cauda equina compression. MRI neuraxis revealed a subdural haematoma. Case 3: A 69-year-old lady presented with a 6-day history of urinary incontinence, right foot drop, and leg weakness. MRI spine was negative for CES. She was diagnosed with an intraparenchymal haematoma of posterior left frontal lobe.

CONCLUSIONS

Negative lumbosacral MRI will not exclude extraspinal mimics of CES and, although rare, these cases should be considered.

Imaging of Neuronal and Mixed Glioneuronal Tumors

Neuronal and mixed glioneuronal tumors represent a group of neoplasms with varying degrees of neural and glial elements. Their age of presentation varies, but they are most commonly seen in children and young adults. With the exception of anaplastic ganglioglioma and other atypical variants, most lesions are low grade; however, they can have significant morbidity because of seizures, mass effect, or difficult to treat hydrocephalus. Although many tumors show overlapping clinical and imaging features, some have relatively distinctive imaging characteristics that may aid in narrowing the differential diagnosis. In this review, we discuss relevant clinical and pathologic characteristics of these tumors and provide an overview of conventional and advanced imaging features that provide clues as to the diagnosis.

Imaging of Fulminant Demyelinating Disorders of the Central Nervous System

We aim to review the imaging appearance of fulminant demyelinating disorders of central nervous system that have different pathological features, clinical course, clinical features, and imaging findings different from classic multiple sclerosis. Routine magnetic resonance imaging (MRI) can help in accurate localization of the lesions, detection of associated lesions, and monitoring of these patients. Advanced MRI combined with routine MRI can aid in differentiation fulminant demyelinating lesions from simulating malignancy. Tumefactive demyelination lesions are located in supratentorial white matter mainly frontal and parietal regions with incomplete rim enhancement. Baló concentric sclerosis shows characteristic concentric onion skin appearance. Schilder disease is subacute or acute demyelinating disorders with one or more lesions commonly involving the centrum semiovale. Marburg disease is the most severe demyelinating disorder with diffuse infiltrative lesions and massive edema involving both the cerebral hemisphere and brain stem.

Liver Imaging Reporting and Data System Version 2018: What Radiologists Need to Know

In this article, we aim to review Liver Imaging Reporting and Data System version 18 (LI-RADS v2018). Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy. Liver Imaging Reporting and Data System developed for standardizing interpreting, reporting, and data collection of HCC describes 5 major features for accurate HCC diagnosis and several ancillary features, some favoring HCC in particular or malignancy in general and others favoring benignity. Untreated hepatic lesions LI-RADS affords 8 unique categories based on imaging appearance on computed tomography and magnetic resonance imaging, which indicate the possibility of HCC or malignancy with or without tumor in vein. Furthermore, LI-RADS defines 4 treatment response categories for treated HCCs after different locoregional therapy. These continuous recent updates on LI-RADS improve the communication between the radiologists and the clinicians for better management and patient outcome.

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose

To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas.

Material and methods

A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas.

Results

There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10^-3 mm²/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%.

Conclusion

TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

Entrapment of the temporal horn secondary to postoperative gamma-knife radiosurgery in intraventricular meningioma: A case report

BACKGROUND

Entrapment of the temporal horn (ETH) is a rare pathologic condition. It is a kind of focal hydrocephalus caused by obstruction of flow pathway of cerebrospinal fluid. It is caused by various conditions, but ETH secondary to postoperative gamma-knife radiosurgery (GKS) is extremely rare.

CASE SUMMARY

A 52-year old previously healthy woman underwent resection of a large intraventricular meningioma. A small fragment of residual tumor with no obvious enlargement of the temporal horn was observed 3 mo after surgery, and she was referred for GKS. Two months after GKS, she complained of headache and progressive paralysis of the left limb. Magnetic resonance imaging revealed enlargement of the temporal horn. There was a second procedure to resect the residual tumor 8 mo after GKS. After the second procedure, she recovered smoothly. As of the date of this writing, she has remained in good condition.

CONCLUSION

This case reminds us that ETH should be considered in the treatment of intraventricular meningiomas, especially before GKS.

Imaging-guided precision medicine in glioblastoma patients treated with immune checkpoint modulators: research trend and future directions in the field of imaging biomarkers and artificial intelligence

Immunotherapies that employ immune checkpoint modulators (ICMs) have emerged as an effective treatment for a variety of solid cancers, as well as a paradigm shift in the treatment of cancers. Despite this breakthrough, the median survival time of glioblastoma patients has remained at about 2 years. Therefore, the safety and anti-cancer efficacy of combination therapies that include ICMs are being actively investigated. Because of the distinct mechanisms of ICMs, which restore the immune system’s anti-tumor capacity, unconventional immune-related phenomena are increasingly being reported in terms of tumor response and progression, as well as adverse events. Indeed, immunotherapy response assessments for neuro-oncology (iRANO) play a central role in guiding cancer patient management and define a “wait and see strategy” for patients treated with ICMs in monotherapy with progressive disease on MRI. This article deciphers emerging research trends to ameliorate four challenges unaddressed by the iRANO criteria: (1) patient selection, (2) identification of immune-related phenomena other than pseudoprogression (i.e., hyperprogression, the abscopal effect, immune-related adverse events), (3) response assessment in combination therapies including ICM, and (4) alternatives to MRI. To this end, our article provides a structured approach for standardized selection and reporting of imaging modalities to enable the use of precision medicine by deciphering the characteristics of the tumor and its immune environment. Emerging preclinical or clinical innovations are also discussed as future directions such as immune-specific targeting and implementation of artificial intelligence algorithms.

Value of Apparent Diffusion Coefficient (ADC) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentially Diagnosing Angiomatous Meningiomas and Solitary Fibrous Tumors/Hemangiopericytomas

Background

To determine if ADC and DCE-MRI can be used to differentiate angiomatous meningiomas (AMs) from solitary fibrous tumors/hemangiopericytomas (SFT/HPCs).

Material/Methods

We retrospectively reviewed records of 103 patients from 1 January 1 2014 to 1 November 2018. We enrolled 41 patients who had undergone a 3T MRI, with histologically confirmed AMs in 20 (48.80%) patients, and SFT/HPCs in 21 (51.20%) patients. The ADC and DCE-MRI features were derived and then compared by 2 independent-samples t tests and Wilcoxon rank sum test to obtain the ROC.

Results

AMs had significantly lower ADC values than did SFT/HPCs, but AMs had significantly higher MCER values than did SFT/HPCs. A threshold value of 1.03×10⁻³ mm²/s for ADC to predict AMs from SFT/HPCs was estimated (AUC=0.902, sensitivity=88.20%, specificity=83.30%). Optimal diagnostic performance (AUC=0.825, sensitivity=84.60%, specificity=81.80%) was obtained when setting MCER=226.7% as the threshold value.

Conclusions

The ADC values of AMs were lower than those of SFT/HPCs; the MCER of AMs were greater than those of SFT/HPCs, and ADC was more useful than MCER, and these parameters could help diagnosis.

Differences between primary central nervous system lymphoma and glioblastoma: topographic analysis using voxel-based morphometry

AIM

To evaluate the diagnostic feasibility of probabilistic analysis using voxel-based morphometry (VBM) in differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM).

MATERIALS AND METHODS

In total, 118 patients with GBM (57 males, 61 females; mean [± standard deviation] age, 56.9±19.3 years; median, 61 years) and 52 patients with PCNSL (37 males, 15 females; mean age, 62±13.3 years, median, 66 years) were studied retrospectively. Each patient underwent preoperative contrast-enhanced T1-weighted imaging (CE-T1WI) using a 1.5 or 3 T magnetic resonance imaging (MRI) system. To assess preferential occurrence sites, images from CE-T1WI were co-registered and spatially normalised using the MNI152 T1 template. Subsequently, a region of interest (ROI) was placed in the centre of the enhancing tumour in normalised images with 1-mm isotropic resolution. The same ROI between normalised and T1 template images was set up using an ROI manager function in ImageJ software. A spherical volume of interest (VOI) with a radius of 10 mm was determined. A probability map was created by overlaying each image with the VOI. Each VOI was removed from T1 template images for VBM analysis. VBM analysis was performed using statistical parametric mapping (SPM) 12 software under default settings.

RESULTS

VBM analysis showed significantly higher frequency in the splenium of the corpus callosum among PCNSL patients than among GBM patients (p<0.05; family-wise error correction).

CONCLUSION

Topographic analysis using VBM provides useful information for differentiating PCNSL from GBM.