Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging

Arterial Spin Labeling: Key Concepts and Progress Towards Use as a Clinical Tool

Arterial spin labeling (ASL), a non-invasive MRI technique, has emerged as a valuable tool for researchers that can measure blood flow and related parameters. This review aims to provide a qualitative overview of the technical principles and recent developments in ASL and to highlight its potential clinical applications. A growing literature demonstrates impressive ASL sensitivity to a range of neuropathologies and treatment responses. Despite its potential, challenges persist in the translation of ASL to widespread clinical use, including the lack of standardization and the limited availability of comprehensive training. As experience with ASL continues to grow, the final stage of translation will require moving beyond single site observational studies to multi-site experience and measurement of the added contribution of ASL to patient care and outcomes.

Balanced transformer: efficient classification of glioblastoma and primary central nervous system lymphoma

Objective.Primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) are malignant primary brain tumors with different biological characteristics. Great differences exist between the treatment strategies of PCNSL and GBM. Thus, accurately distinguishing between PCNSL and GBM before surgery is very important for guiding neurosurgery. At present, the spinal fluid of patients is commonly extracted to find tumor markers for diagnosis. However, this method not only causes secondary injury to patients, but also easily delays treatment. Although diagnosis using radiology images is non-invasive, the morphological features and texture features of the two in magnetic resonance imaging (MRI) are quite similar, making distinction with human eyes and image diagnosis very difficult. In order to solve the problem of insufficient number of samples and sample imbalance, we used data augmentation and balanced sample sampling methods. Conventional Transformer networks use patch segmentation operations to divide images into small patches, but the lack of communication between patches leads to unbalanced data layers.Approach.To address this problem, we propose a balanced patch embedding approach that extracts high-level semantic information by reducing the feature dimensionality and maintaining the geometric variation invariance of the features. This approach balances the interactions between the information and improves the representativeness of the data. To further address the imbalance problem, the balanced patch partition method is proposed to increase the receptive field by sampling the four corners of the sliding window and introducing a linear encoding component without increasing the computational effort, and designed a new balanced loss function.Main results.Benefiting from the overall balance design, we conducted an experiment using Balanced Transformer and obtained an accuracy of 99.89%, sensitivity of 99.74%, specificity of 99.73% and AUC of 99.19%, which is far higher than the previous results (accuracy of 89.6% ∼ 96.8%, sensitivity of 74.3% ∼ 91.3%, specificity of 88.9% ∼ 96.02% and AUC of 87.8% ∼ 94.9%).Significance.This study can accurately distinguish PCNSL and GBM before surgery. Because GBM is a common type of malignant tumor, the 1% improvement in accuracy has saved many patients and reduced treatment times considerably. Thus, it can provide doctors with a good basis for auxiliary diagnosis.

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics - Methodological Overview and Use Cases

BACKGROUND

 Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF).

METHOD

 This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine).

RESULTS

 ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels.

CONCLUSION

 Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring.

KEY POINTS

  · ASL is an MRI technique that uses labeled blood-water as an endogenous tracer for brain perfusion imaging.. · It allows the assessment of CBF without the need for administration of a gadolinium-based contrast agent.. · CBF quantification by ASL has been used in several pathologies including brain tumors or neurodegenerative diseases.. · Vessel-selective ASL methods can provide brain perfusion territory mapping in cerebrovascular diseases.. · ASL may be of particular interest in patient cohorts with caveats concerning gadolinium administration..

Fractionated Radiotherapy After Gross Total Resection of Clival Chordoma: A Systematic Review of Survival Outcomes

BACKGROUND

Current treatment guidelines for clival chordomas recommend surgical resection followed by high-dose radiotherapy (RT). However, in patients in whom gross total resection (GTR) is achieved, the benefits of additional RT remain unclear.

OBJECTIVE

To investigate whether RT offers any benefit to progression-free survival (PFS) in patients undergoing GTR of clival chordoma by performing a systematic review of all currently published literature.

METHODS

A total of 5 databases were searched to include all studies providing data on GTR ± RT for clival chordomas (January 1990-June 2021). Qualitative assessment was performed with Newcastle-Ottawa Scale guidelines for assessing quality of nonrandomized studies. Statistical analysis using individualized patient data of PFS was performed.

RESULTS

The systematic search yielded 2979 studies, weaned to 22 full-text articles containing 108 patients. All patients underwent GTR of clival chordoma, with 46 (43%) patients receiving adjuvant RT. Mean PFS for RT patients was 31.09 months (IQR: 12.25-37.75) vs 54.92 months (IQR: 14.00-85.75) in non-RT patients. Overall, RT did not increase PFS (HR 0.320, P = .069) to a value that achieved statistical significance. Stratifying by photon therapy vs particle beam therapy yielded no statistically significant benefit for particle beam therapy for PFS ( P = .300). Of patients with age ≥65 years, RT did not improve outcomes to statistical significance for PFS (HR 0.450, P = .481). Patients age ≥65 years had lower PFS on both bivariate analysis (HR 3.708, P = .007) and multivariate analysis (HR 3.322, P = .018).

CONCLUSION

After achieving GTR of clival chordoma, fractionated RT offers unclear benefit upon survival outcomes.

Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling

OBJECTIVES

To evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM).

METHODS

One hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADCmin), mean (rADCmean), maximum (rADCmax), and rADCmax-min (rADCdif) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBFmax) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance.

RESULTS

Atypical PCNSLs showed significantly lower rADCmax, rADCmean, and rADCdif than that of GBMs. GBMs showed significantly higher CBFmax and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADCmean, CBFmax, and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively.

CONCLUSION

The combined evaluation of rADCmean, CBFmax, and nET volume allowed for reliable differentiation between atypical PCNSL and GBM.

KEY POINTS

• Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection. • The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma. • The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Arterial spin labeling clinical applications for brain tumors and tumor treatment complications: A comprehensive case-based review

Arterial spin labeling (ASL) is a noninvasive neuroimaging technique that allows for quantifying cerebral blood flow without intravenous contrast. Various neurovascular disorders and tumors have cerebral blood flow alterations. Identifying these perfusion changes through ASL can aid in the diagnosis, especially in entities with normal structural imaging. In addition, complications of tumor treatment and tumor progression can also be monitored using ASL. In this case-based review, we demonstrate the clinical applications of ASL in diagnosing and monitoring brain tumors and treatment complications.

Arterial spin labeling of nasopharyngeal carcinoma shows early therapy response

OBJECTIVE

This study aimed to determine the value of arterial spin labeling (ASL) perfusion imaging in assessing the early efficacy of chemoradiotherapy for nasopharyngeal carcinoma (NPC).

METHODS

Fifty-five patients with locoregionally advanced NPC underwent conventional 3.0-T magnetic resonance imaging (MRI) and ASL before and after chemoradiotherapy (prescribed dose reached 40 Gy). Based on the response evaluation criteria for solid tumors (RECIST 1.1), the patients were divided into the partial response and stable disease groups. MRI re-examination was performed one month after chemoradiotherapy completion, and patients were divided into residual and non-residual groups. We investigated inter-group differences in ASL-based tumor blood flow (TBF) parameters (pre-treatment tumor blood flow, post-treatment tumor blood flow, and changes in tumor blood flow, i.e., Pre-TBF, Post-TBF, ΔTBF), correlation between TBF parameters and tumor atrophy rate, and value of TBF parameters in predicting sensitivity to chemoradiotherapy.

RESULTS

There were differences in Pre-TBF, Post-TBF, and ΔTBF between the partial response and stable disease groups (p < 0.01). There were also differences in Pre-TBF and ΔTBF between the residual and non-residual groups (p < 0.01). Pre-TBF and ΔTBF were significantly correlated with the tumor atrophy rate; the correlation coefficients were 0.677 and 0.567, respectively (p < 0.01). Pre-TBF had high diagnostic efficacies in predicting sensitivity to chemoradiotherapy and residual tumors, with areas under the curve of 0.845 and 0.831, respectively.

CONCLUSION

ASL permits a noninvasive approach to predicting the early efficacy of chemoradiotherapy for NPC.

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.

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

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

3D pseudo-continuous arterial spin labeling-MRI (3D PCASL-MRI) in the differential diagnosis between glioblastomas and primary central nervous system lymphomas

PURPOSE

The aim of the study was to compare the parameters of blood flow in glioblastomas and primary central nervous system lymphomas (PCNSLs), measured by pseudo-continuous arterial spin labeling MRI (3D PCASL), and to determine the informativeness of this method in the differential diagnosis between these lesions.

METHODS

The study included MRI data of 139 patients with PCNSL (n = 21) and glioblastomas (n = 118), performed in the Burdenko Neurosurgical Center. No patients received chemotherapy, hormone therapy, or radiation therapy prior to MRI. On the 3D PCASL perfusion map, the absolute and normalized values of tumor blood flow were calculated in the glioblastoma and PCNSL groups (maxTBFmean and nTBF).

RESULTS

MaxTBFmean and nTBF in the glioblastoma group were significantly higher than those in the PCNSL group: 168.9 ml/100 g/min versus 65.6 and 9.3 versus 3.7, respectively (p < 0.001). Arterial spin labeling perfusion had high sensitivity (86% for maxTBFmean, 95% for nTBF) and specificity (77% for maxTBFmean, 73% for nTBF) in the differential diagnosis between PCNSL and glioblastomas. Blood flow thresholds were 98.9 ml/100 g/min using absolute blood flow values and 6.1 using normalized values, AUC > 0.88.

CONCLUSION

The inclusion of 3D PCASL in the standard MRI protocol can increase the specificity of the differential diagnosis between glioblastomas and PCNSL.

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.

Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively. Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design. Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues. Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice. Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Differentiation of Acquired Immune Deficiency Syndrome Related Primary Central Nervous System Lymphoma from Cerebral toxoplasmosis with Use of Susceptibility-Weighted Imaging and Contrast Enhanced 3D-T1WI

BACKGROUND

We aimed to investigate whether susceptibility-weighted imaging (SWI) and contrast-enhanced 3D-T1WI can differentiate Acquired Immune Deficiency Syndrome-Related Primary Central Nervous System Lymphoma (AR-PCNSL) from cerebral toxoplasmosis.

METHODS

This was a prospective cohort study. 20 AIDS patients were divided into AR-PCNSL group (13 cases) and cerebral toxoplasmosis group (7 cases) based on pathology results. We analyzed the appearance of lesions on SWI and enhanced 3D T1WI and ROC curves in the diagnosis of AR-PCNSL and cerebral toxoplasmosis.

RESULTS

Cerebral toxoplasmosis was more likely to show annular enhancement (p = 0.002) and complete smooth ring enhancement (p = 0.002). It was also more likely to present a complete, smooth low signal intensity rim (LSIR) (p = 0.002) and an incomplete, smooth LSIR (p = 0.019) on SWI. AR-PCNSL was more likely to present an incomplete, irregular LSIR (p < 0.001) and irregular central low signal intensity (CLSI) (p<0.001) on SWI. The areas under the ROC curve of the SWI-ILSS grade and enhanced volume on 3D-T1WI were 0.872 and 0.862, respectively.

CONCLUSION

A higher SWI-ILSS grade and larger 3D-T1WI volume enhancement were diagnostic for AR-PCNSL. SWI and CE 3D-T1WI were useful in the differential diagnosis of AR-PCNSL and cerebral toxoplasmosis.

Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion

Objectives

To explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL).

Materials and Methods

This retrospective study analyzed the MRI data of 261 patients with pathologically diagnosed solitary and multiple cerebral GBM (n = 97), PCNSL (n = 92), and TDL (n = 72). The 3D segmentation model was trained to capture the lesion. Different enhancement data were generated by changing the pixel ratio of the lesion and non-lesion areas. The 3D classification network was trained by using the enhancement data. The accuracy, sensitivity, specificity, and area under the curve (AUC) were used to assess the value of different enhancement data on the discrimination performance. These results were then compared with the neuroradiologists’ diagnoses.

Results

The diagnostic performance fluctuated with the ratio of lesion to non-lesion area changed. The diagnostic performance was best when the ratio was 1.5. The AUCs of GBM, PCNSL, and TDL were 1.00 (95% confidence interval [CI]: 1.000–1.000), 0.96 (95% CI: 0.923–1.000), and 0.954 (95% CI: 0.904–1.000), respectively.

Conclusions

Deep learning with data enhancement is useful for the accurate identification of GBM, PCNSL, and TDL, and its diagnostic performance is better than that of the neuroradiologists.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

Cerebral and tumoral blood flow in adult gliomas: a systematic review of results from magnetic resonance imaging

Objective:

Blood flow is the rate of blood movement and relevant to numerous processes, though understudied in gliomas. The aim of this review was to pool blood flow metrics obtained from MRI modalities in adult supratentorial gliomas.

Methods:

MEDLINE, EMBASE and the Cochrane database were queried 01/01/2000–31/12/2019. Studies measuring blood flow in adult Grade II–IV supratentorial gliomas using dynamic susceptibility contrast (DSC) MRI, dynamic contrast enhanced MRI (DCE-MRI) or arterial spin labelling (ASL) were included. Absolute and relative cerebral blood flow (CBF), peritumoral blood flow and tumoral blood flow (TBF) were reported.

Results:

34 studies were included with 1415 patients and 1460 scans. The mean age was 52.4 ± 7.3 years. Most patients had glioblastoma (n = 880, 64.6%). The most common imaging modality was ASL (n = 765, 52.4%) followed by DSC (n = 538, 36.8%). Most studies were performed pre-operatively (n = 1268, 86.8%). With increasing glioma grade (II vs IV), TBF increased (70.8 vs 145.5 ml/100 g/min, p < 0.001) and CBF decreased (85.3 vs 49.6 ml/100 g/min, p < 0.001). In Grade IV gliomas, following treatment, CBF increased in ipsilateral (24.9 ± 1.2 vs 26.1 ± 0.0 ml/100 g/min, p < 0.001) and contralateral white matter (25.6 ± 0.2 vs 26.0± 0.0 ml/100 g/min, p < 0.001).

Conclusion:

Our findings demonstrate that increased mass effect from high-grade gliomas impairs blood flow within the surrounding brain that can improve with surgery.

Advances in knowledge:

This systematic review demonstrates how mass effect from brain tumours impairs blood flow in the surrounding brain parenchyma that can improve with treatment.

Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review

This review addresses the challenges of primary central nervous system (CNS) lymphoma diagnosis, assessment of treatment response, and detection of recurrence. Primary CNS lymphoma is a rare form of extra-nodal non-Hodgkin lymphoma that can involve brain, spinal cord, leptomeninges, and eyes. Primary CNS lymphoma lesions are most commonly confined to the white matter or deep cerebral structures such as basal ganglia and deep periventricular regions. Contrast-enhanced magnetic resonance imaging (MRI) is the standard diagnostic modality employed by neuro-oncologists. MRI often shows common morphological features such as a single or multiple uniformly well-enhancing lesions without necrosis but with moderate surrounding edema. Other brain tumors or inflammatory processes can show similar radiological patterns, making differential diagnosis difficult. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) has selected utility in cerebral lymphoma, especially in diagnosis. Primary CNS lymphoma can sometimes present with atypical findings on MRI and FDG PET, such as disseminated disease, non-enhancing or ring-like enhancing lesions. The complementary strengths of PET and MRI have led to the development of combined PET-MR systems, which in some cases may improve lesion characterization and detection. By highlighting active developments in this field, including advanced MRI sequences, novel radiotracers, and potential imaging biomarkers, we aim to spur interest in sophisticated imaging approaches.

Differentiation between primary CNS lymphoma and atypical glioblastoma according to major genomic alterations using diffusion and susceptibility-weighted MR imaging

PURPOSE

We aimed to differentiate primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (GB) and distinguish major genomic subtypes between these tumors using susceptibility-weighted imaging (SWI) along with diffusion-weighted imaging (DWI).

METHODS

Thirty-one immuno-competent patients with PCNSL stratified by BCL2 and MYC rearrangement, and 57 patients with atypical GB (no visible necrosis) grouped according to isocitrate dehydrogenase-1 (IDH1) mutation status underwent 3.0-Tesla MRI before treatment in this retrospective study. Region of interest analysis with apparent diffusion coefficient (ADC) and SWI signal intensity values of the tumors were normalized by dividing those of contralateral white matter. The independent-samples t-test and Kruskal-Wallis test were utilized to compare parameters. The diagnostic ability of each parameter and their optimal combination was evaluated by logistic regression analysis and receiver operating characteristic.

RESULTS

PCNSL with rearrangement of both MYC and BCL2 (n = 7) [mean relative (r) ADCmean:0.87 ± 0.06, rADCmin:0.72 ± 0.08] demonstrated significantly lower rADCmean, and rADCmin compared to other PCNSLs (n = 24) (rADCmean:1.19 ± 0.18, rADCmin:1.03 ± 0.17;p < 0.001) and GBs (p < 0.001). GB without IDH1 mutation (n = 44) (mean rSWI value:0.95 ± 0.15) demonstrated significantly lower rSWI value compared to GB with IDH1 mutation (n = 13) (rSWI value:1.13 ± 0.09;p < 0.001) and PCNSL (p < 0.001). The incorporation of rADCmean and rSWI parameters distinguished GB with IDH1 mutation [Area under the curve (AUC):0.985] with sensitivity and specificity of 94.3 and 100 % respectively; and PCNSL with rearrangement of both MYC and BCL2 (AUC:0.982) with sensitivity and specificity of 100 % and 95.4 %, respectively. CONCLUSıONS: Combined analysis of SWI and DWI could differentiate atypical GB from PCNSL and distinguish major genomic subtypes between these tumors.

Differentiating between Primary Central Nervous System Lymphoma and Glioblastoma: The Diagnostic Value of Combining 18F-fluorodeoxyglucose Positron Emission Tomography with Arterial Spin Labeling

Using conventional magnetic resonance imaging (MRI) methods, the differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is often difficult due to overlapping imaging characteristics. This study aimed to evaluate the diagnostic value of combining ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) with arterial spin labeling (ASL) for differentiating PCNSL from GBM. In all, 20 patients with PCNSL and 55 with GBM were retrospectively examined. From the FDG-PET data, the maximum standardized uptake values (SUVmax) and the ratio of tumor to normal contralateral gray matter (T/N_SUVmax) were calculated. From the ASL data, the T/N ratio of the maximum tumor blood flow (relative TBFmax: rTBFmax) was obtained. Diagnostic performance of each parameter was analyzed using univariate and multivariate logistic regression analyses and receiver-operating characteristic (ROC) curve analyses. A generalized linear model was applied for comparing the performance of FDG-PET and ASL individually, and in combination. In univariate analysis, SUVmax and T/N_SUVmax were statistically higher in patients with PCNSL and rTBFmax was higher in patients with GBM. In the multivariate analysis, T/N_SUVmax and rTBFmax were statistically independent. The sensitivity, specificity, and area under the curve (AUC) for discriminating PCNSL from GBM were 100%, 87.3%, and 0.950 in T/N_SUVmax; 90%, 72.7%, and 0.824 in rTBFmax; and 95%, 96.4%, and 0.991 in the combined model, respectively. The combined use of T/N_SUVmax and rTBFmax may contribute to better differentiation between PCNSL and GBM.

A comparative study of multimodal magnetic resonance in the differential diagnosis of acquired immune deficiency syndrome related primary central nervous system lymphoma and infection

Background

Patients with acquired immune deficiency syndrome (AIDS) often suffer from opportunistic infections and related primary central nervous system lymphoma (AR-PCNSL). Both diseases showed multiple ring enhancement lesions in conventional magnetic resonance (MR). It is very difficult to make the differential diagnosis. We aimed to investigate whether multimodal MR (diffusion weighted imaging (DWI)/ apparent diffusion coefficient (ADC), 3D pseudo-continuous arterial spin labeling (3D-pCASL) and susceptibility-weighted imaging (SWI)) combined with conventional MR can differentiate AR-PCNSL from infections.

Methods

This was a prospective study. We recruited 19 AIDS patients who were divided into AR-PCNSL group (9 cases) and infection group (10 cases) by pathological results. We analyzed whether there was statistical (Fisher’s method) difference in multimodal MR between the two groups. We analyzed whether multimodal MR combined with conventional MR could improve the diagnosis of AR-PCNSL.

Results

The lesions were more likely involved the paraventricular (0.020) and corpus callosum (0.033) in AR-PCNSL group in conventional MR. In multimodal MR, AR-PCNSL group showed low ADC value, with p values of 0.001. Infection group more inclined to high ADC value, with p was 0.003. In multimodal MR, AR-PCNSL group had more low signal intensity (grade 2–3) in the degree of intratumoral susceptibility signal intensity in SWI (SWI-ITSS), with p values of 0.001. The sensitivity, specificity of conventional MR in the diagnosis of AR-PCNSL was 88.9 and 70.0%. The conventional MR sequence combined with DWI/ADC sequence in the diagnosis of AR-PCNSL had a sensitivity of 100.0%, and a specificity of 60.0%. The sensitivity and specificity of the conventional MR sequence combined with the SWI-ITSS sequence in the diagnosis of AR-PCNSL were 100 and 70.0%. The conventional MR combined with ADC or SWI-ITSS improved the diagnosis of AR-PCNSL.

Conclusion

Multimodal MR could distinguish AR-PCNSL from infectious lesions. The multimodal MR (DWI/ADC or SWI-ITSS) combined with conventional MR could improve the diagnosis of AR-PCNSL. The ADC value should be attached importance in clinical work. When distinguishing AR-PCNSL from toxoplasmosis or tuberculoma, SWI should be used to obtain a correct diagnosis.

Differentiating Glioblastoma from Primary Central Nervous System Lymphoma: The Value of Shaping and Nonenhancing Peritumoral Hyperintense Gyral Lesion on FLAIR Imaging

BACKGROUND

This study describes a distinct magnetic resonance imaging (MRI) feature, placing emphasis on fluid-attenuation inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1C) images for the preoperative differentiation of glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL).

METHODS

The preoperative MRI findings of 116 pathologically confirmed glioblastoma (n = 72) and PCNSL (n = 44) were retrospectively reviewed. Two neuroimaging specialists analyzed the MRIs, and image analysis was focused on the presence or absence of a shaping and nonenhancing peritumoral hyperintense gyral lesion on FLAIR imaging (SNEPGF, i.e., hyperintense lesion in a shaping and nonenhancing peritumoral gyral area on FLAIR imaging). The gyral area adjacent to and within 3 cm of the enhanced tumor was defined as the peritumoral gyrus region. The FLAIR hyperintensity lesion were termed as the signal intensity ratio ≥30% compared with contralateral normal gray matter. Then, the differential diagnostic efficacy of SNEFPG sign for GBM and PCNSL was analyzed.

RESULTS

The SNEPGF sign was found in 33 GBM cases (33 of 72, 45.8%), and the FLAIR signal intensity and apparent diffusion coefficient value of these area were lower than the peritumoral edema area (P < 0.0001). In 44 PCNSL cases, no SNEPGF sign was found. A slightly higher FLAIR signal intensity was seen in 9 PCNSLs, but uniform and marked enhancement was seen in these areas. The sensitivity, specificity, positive predictive value, and negative predictive value of the differential diagnosis of GBM and PCNSL with SNEPGF sign were 45.8%, 100%, 100%, and 53.0%, respectively.

CONCLUSIONS

The SNEPGF sign is effective in identifying GBM from PCNSL, especially with high specificity.

Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma?

BACKGROUND

Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low.

PURPOSE

To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx.

STUDY TYPE

Retrospective.

POPULATION

Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed.

FIELD STRENGTH/SEQUENCE

3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery_- fast spoiled gradient recalled echo brain volume (BRAVO) imaging.

ASSESSMENT

Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated.

STATISTICAL TESTS

Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter.

RESULTS

Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively.

DATA CONCLUSION

For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Pseudo-continuous arterial spin labelling shows high diagnostic performance in the detection of postoperative residual lesion in hyper-vascularised adult brain tumours

OBJECTIVES

Our aim was to evaluate the contribution of pseudo-continuous arterial spin labelling (pCASL) in the detection of a postoperative residual lesion in adult brain tumours.

METHODS

Seventy-five patients were prospectively included. Following the results of preoperative DSC-PWI assessment, intra-axial lesions, including high-grade gliomas (n = 43) and certain metastases (n = 14), were classified as hyper-vascular (HV+ group, n = 57); other lesions, including low-grade gliomas and certain metastases, were classified as non-hyper-vascular (HV- group, n = 18). To confirm the absence/presence of a residual lesion or disease progression, postoperative MRI including pCASL sequence and follow-up-MRI were performed within 72 h and 1-6 months after the resection, respectively. Two raters evaluated the images. Mean and maximal ASL cerebral blood flow (CBF) values were measured in the perioperative region and normalised to the contralateral tissue. The pCASL-CBF maps and post-contrast T1WI were visually assessed for residual lesion. Quantitative data were analysed with unpaired Student t and Mann-Whitney U tests and the visual diagnostic performance with the McNemar test.

RESULTS

In the HV+ group, the mean normalised CBF was 1.97 ± 0.59 and 0.97 ± 0.29 (p < 0.0001, AUC = 0.964, cut-off = 1.27) for patients with or without residual tumours, respectively. The mean normalised CBF was not discriminative for assessing residual tumours in the HV- group (p = 0.454). Visual CBF evaluation allowed 92.98% patients belonging to the HV+ group to be correctly classified (sensitivity 93.02%, specificity 92.86%, p < 0.001). Visual evaluation was correlated with contrast enhancement evaluation and with the mean normalised CBF values (r = 0.505, p < 0.0001 and 0.838, p < 0.0001, respectively).

CONCLUSION

Qualitative and quantitative ASL evaluation shows high diagnostic performance in postoperative assessment of hyper-perfused tumours. In this case, postoperative pCASL may be useful, especially if contrast injection cannot be performed or when contrast enhancement is doubtful.

KEY POINTS

• Evaluation of postoperative residual lesion in the case of brain tumours is an imaging challenge. • This prospective monocentric study showed that increased normalised cerebral blood flow assessed by pseudo-continuous arterial spin labelling (pCASL) correlates well with the presence of a residual tumour in the case of hyper-vascular tumour diagnosed on preoperative MRI. • Qualitative and quantitative pCASL is an informative sequence for hyper-vascular residual tumour, especially if acquired more than 48 h after brain tumour surgery, when contrast enhancement can give ambiguous results due to blood-brain barrier disruption.

Jugular venous reflux may mimic type I dural arterio-venous fistula on arterial spin labeling magnetic resonance images

PURPOSE

Previous studies have shown that arterial spin-labeling (ASL) has high sensitivity and specificity for detecting dural arteriovenous fistulas (DAVFs). However, in case of jugular venous reflux (JVR), the labeled protons in the jugular vein may lead to a venous hypersignal in the jugular vein, sigmoid, and transverse sinus on ASL images and mimic DAVF.

METHODS

To ascertain this hypothesis, two blinded senior neuroradiologists independently and retrospectively reviewed randomized ASL images and graded the likelihood of DAVF on a 5-point Likert scale in 2 groups of patients: (i) 13 patients with angiographically proven type I DAVF; and (ii) 11 patients with typical JVR diagnosed on the basis of clinical and MR imaging data, first using ASL alone, and second using ASL together with all of the sequences including 4D CE MRA.

RESULT

A dural venous ASL signal was seen in 11 patients with type I DAVF and in all the 11 patients with JVR, with no distinctive pattern between the two. The mean Likert score was "very likely" in DAVF and JVR patients when using ASL alone (k = 0.71), and "very unlikely" for JVR versus "very likely" for DAVF when using all the sequences available (k = 0.92).

CONCLUSION

Our study shows that JVR can mimic DAVF on ASL images with potential implications for patient care. The detection of DAVFs should be based on additional MR sequences such as TOF-MRA and 4D CE MRA to exclude JVR and to avoid unnecessary DSAs.

Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview

Gadolinium-based contrast agents (GBCAs) increase lesion detection and improve disease characterization for many cerebral pathologies investigated with MRI. These agents, introduced in the late 1980s, are in wide use today. However, some non-ionic linear GBCAs have been associated with the development of nephrogenic systemic fibrosis in patients with kidney failure. Gadolinium deposition has also been found in deep brain structures, although it is of unclear clinical relevance. Hence, new guidelines from the International Society for Magnetic Resonance in Medicine advocate cautious use of GBCA in clinical and research practice. Some linear GBCAs were restricted from use by the European Medicines Agency (EMA) in 2017.

This review focuses on non-contrast-enhanced MRI techniques that can serve as alternatives for the use of GBCAs. Clinical studies on the diagnostic performance of non-contrast-enhanced as well as contrast-enhanced MRI methods, both well established and newly proposed, were included. Advantages and disadvantages together with the diagnostic performance of each method are detailed. Non-contrast-enhanced MRIs discussed in this review are arterial spin labeling (ASL), time of flight (TOF), phase contrast (PC), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), susceptibility weighted imaging (SWI), and amide proton transfer (APT) imaging.

Ten common diseases were identified for which studies reported comparisons of non-contrast-enhanced and contrast-enhanced MRI. These specific diseases include primary brain tumors, metastases, abscess, multiple sclerosis, and vascular conditions such as aneurysm, arteriovenous malformation, arteriovenous fistula, intracranial carotid artery occlusive disease, hemorrhagic, and ischemic stroke.

In general, non-contrast-enhanced techniques showed comparable diagnostic performance to contrast-enhanced MRI for specific diagnostic questions. However, some diagnoses still require contrast-enhanced imaging for a complete examination.

Accuracy of apparent diffusion coefficients and enhancement ratios on magnetic resonance imaging in differentiating primary cerebral lymphomas from glioblastoma

BACKGROUND AND PURPOSE

This study aimed to determine the accuracy of apparent diffusion coefficient (ADC) and enhancement ratio (ER) in discriminating primary cerebral lymphomas (PCL) and glioblastomas.

MATERIALS AND METHODS

Circular regions of interest were randomly placed centrally within the largest solid-enhancing area of all lymphomas and glioblastomas on both post-contrast T1-weighted images and corresponding ADC maps. Regions of interest were also drawn in the contralateral hemisphere to obtain enhancement and ADC values of normal-appearing white matter. This helped us to calculate the ER and ADC ratio.

RESULTS

Mean enhancement and ADC (mm²/s) values for PCL were 2220.56 ± 2948.30 and 712.00 ± 137.87, respectively. Mean enhancement and ADC values for glioblastoma were 1537.07 ± 1668.33 and 1037.93 ± 280.52, respectively. Differences in ADC values, ratios and ERs were all statistically significant between the two groups (p < 0.05). ADC values correctly predicted 71.4% of the lesions as glioblastoma and 83.3% as PCL (area under the curve (AUC) = 0.86 on receiver operating characteristic curve analysis). ADC ratios correctly predicted 85.7% of the lesions as glioblastoma and 100% as PCL (AUC = 0.93). ERs correctly predicted 71.4% of the lesions as glioblastoma and 88.9% as PCL (AUC = 0.92). The combination of ADC ratio and ER correctly predicted 100% tumour type in both patient subgroups.

CONCLUSIONS

ADC values, ADC ratios and ERs may serve as useful variables to distinguish PCL from glioblastoma. The combination of ADC ratio and ER yielded the best results in identification of both patient subgroups.

MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis

BACKGROUND

Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.

PURPOSE

To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

Ovid-MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."

FIELD STRENGTH/SEQUENCE

Patients underwent at least one MRI sequence including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced imaging (DSC), dynamic contrast-enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.

ASSESSMENT

Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

STATISTICAL TESTS

Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta-regression was performed.

RESULTS

Twenty-two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87-93%) and specificity of 89% (95% CI, 85-93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90-0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89-97%] and specificity of 91% [95% CI, 86-96%]). Heterogeneity was only detected in specificity (I² = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.

DATA CONCLUSION

MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:560-572.

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

OBJECTIVE

Differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma using arterial spin labeling perfusion and diffusion tensor imaging (DTI).

METHODS

We performed a prospective study of 31 patients with a provisional diagnosis of PCNSL and glioblastoma who underwent conventional magnetic resonance imaging, DTI, and arterial spin labeling of the brain. The tumor blood flow (TBF), mean diffusivity (MD) plus fractional anisotropy (FA) of the mass were measured. The final diagnosis was confirmed by pathological examination.

RESULTS

The TBF of PCNSL (26.41 ± 4.03 mL/100 g/minute) was significantly lower than that of glioblastoma (51.08 ± 3.9 mL/100 g/minute; P = 0.001). The TBF cutoff (35.73 mL/100 g/minute) used for differentiation showed area under the curve (AUC) of 0.93, accuracy of 95.2%, sensitivity of 91.7%, and specificity of 100%. The MD of PCNSL (0.87 ± 0.2X 10^-3 mm²/second) was significantly lower than that of glioblastoma (0.87 ± 0.2 × 10^-3 mm²/second; P = 0.01). The MD cutoff (0.935 × 10^-3 mm²/second) used for differentiation showed an AUC of 0.73 and accuracy of 66.7% and a sensitivity of 75% and specificity of 55.6%. The FA of PCNSL (0.253 ± 0.05) was significantly greater than that of glioblastoma (0.135 ± 0.06; P = 0.001). The FA cutoff (0.185) used for differentiation revealed an AUC of 0.944 and accuracy of 85.7% and a sensitivity of 83.3% and specificity of 88.9%. The combined TBF, MD, and FA cutoffs revealed an AUC of 0.96 and accuracy of 95.5% and a sensitivity of 83.3% and specificity of 100%.

CONCLUSION

The noninvasive imaging parameters using TBF and DTI might help in differentiating PCNSL from glioblastoma.

Utility of arterial spin labelling MRI for discriminating atypical high-grade glioma from primary central nervous system lymphoma

AIM

To evaluate the ability of arterial spin labelling (ASL) magnetic resonance imaging (MRI) in differentiating primary central nervous system lymphoma (PCNSL) from atypical high-grade glioma (HGG), as well as exploring the underlying pathological mechanisms.

METHODS AND MATERIALS

Twenty-three patients with PCNSL and 17 patients with atypical HGG who underwent ASL-MRI were identified retrospectively. Absolute cerebral blood flow (aCBF) and normalised cerebral blood flow (nCBF) values were obtained, and were compared between PCNSL and atypical HGG using the Mann-Whitney U-test. The performance in discriminating between PCNSL and atypical HGG was evaluated using receiver-operating characteristics analysis and area-under-the-curve (AUC) values for aCBF and nCBF. The correlation between microvessel density (MVD) and aCBF was determined by Spearman's correlation analysis.

RESULTS

Atypical HGG demonstrated significantly higher aCBF, nCBF, and MVD values than PCNSL (p<0.05). The diagnostic accuracy of discriminating PCNSL from atypical HGG showed AUC=0.877 (95% confidence interval [CI] 0.735-0.959) for aCBF, and AUC=0.836 (95% confidence interval [CI] 0.685-0.934) for nCBF. There was a moderate positive correlation between aCBF values of region of interest (ROI >30 mm²) in the enhanced area and MVD values (rho=0.579, p=0.0001), and a strong positive correlation between aCBF values MVD based on "point-to-point biopsy" (rho=0.83, p=0.0029). Interobserver agreements for aCBF and nCBF were excellent (ICC >0.75).

CONCLUSIONS

ASL perfusion MRI is a useful imaging technique for the discrimination between atypical HGG and PCNSL, which may be determined by the difference of MVD between them.