Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma

Postoperative disappearance of leptomeningeal enhancement around the brainstem in glioblastoma

PURPOSE

Leptomeningeal enhancement (LME) suggests leptomeningeal dissemination (LMD) of tumor cells, which is a complication of end-stage glioblastoma, and is associated with a poor prognosis. However, magnetic resonance imaging (MRI) occasionally indicates the disappearance of peri-brainstem LME after surgical resection of glioblastoma. Since preoperative LMD may affect treatment indications, we aimed to analyze the clinical significance of preoperative LME of the brainstem in glioblastoma.

METHODS

We retrospectively collected clinical and radiological data from consecutive patients with glioblastoma and preoperative LME of the brainstem, who were treated at our hospital between 2017 and 2020.

RESULTS

Among 112 patients with glioblastoma, nine (8%) showed preoperative LME of the brainstem. In comparison with tumors without LME, tumor size was significantly associated with the preoperative LME of the brainstem (p = 0.016). In addition, there was a trend toward significance for a relationship between deep tumor location and preoperative LME of the brainstem (p = 0.058). Notably, among six patients who underwent surgical resection for glioblastoma with LME of the brainstem, four showed significant radiological disappearance of the LME on postoperative MRI. This suggests that the LME did not result from LMD in these cases. Moreover, these four patients lived longer than would be expected from the presence of LMD. However, this LME disappearance was not observed after biopsy or chemoradiotherapy.

CONCLUSIONS

These findings suggest that preoperative LME does not necessarily indicate the presence of untreatable LMD; moreover, LME may disappear after surgical tumor resection. Thus, transient preoperative LME could be attributed to other mechanisms, including impaired venous flow due to intratumoral arteriovenous shunts, which can be resolved by reducing the tumor burden.

Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma

OBJECTIVE

This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma.

METHODS

Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis.

RESULTS

The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001).

CONCLUSION

With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma.

CLINICAL RELEVANCE STATEMENT

High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL.

KEY POINTS

• Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.

Micro RNAs as a Diagnostic Marker between Glioma and Primary CNS Lymphoma: A Systematic Review

Differentiating glioma from primary central nervous system lymphoma (PCNSL) can be challenging, and current diagnostic measures such as MRI and biopsy are of limited efficacy. Liquid biopsies, which detect circulating biomarkers such as microRNAs (miRs), may provide valuable insights into diagnostic biomarkers for improved discrimination. This review aimed to investigate the role of specific miRs in diagnosing and differentiating glioma from PCNSL. A systematic search was conducted of PubMed, Scopus, Web of Science, and Embase for articles on liquid biopsies as a diagnostic method for glioma and PCNSL. Sixteen dysregulated miRs were identified with significantly different levels in glioma and PCNSL, including miR-21, which was the most prominent miR with higher levels in PCNSL, followed by glioma, including glioblastoma (GBM), and control groups. The lowest levels of miR-16 and miR-205 were observed in glioma, followed by PCNSL and control groups, whereas miR-15b and miR-301 were higher in both tumor groups, with the highest levels observed in glioma patients. The levels of miR-711 were higher in glioma (including GBM) and downregulated in PCNSL compared to the control group. This review suggests that using these six circulating microRNAs as liquid biomarkers with unique changing patterns could aid in better discrimination between glioma, especially GBM, and PCNSL.

Differentiating glioblastoma from primary central nervous system lymphoma of atypical manifestation using multiparametric magnetic resonance imaging: A comparative study

Background

The aim of this study is to evaluate the diagnostic efficiency of magnetic resonance imaging (MRI) of single parameters, unimodality, and bimodality in distinguishing glioblastoma (GBM) from atypical primary central nervous system lymphoma (PCNSL) based on diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC) enhancement, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (¹H-MRS) findings.

Methods

The cohort included 108 patients pathologically diagnosed with GBM and 54 patients pathologically diagnosed with PCNSL. Pretreatment morphological MRI, DWI, DSC, DTI and MRS were all performed on each patient. The quantitative parameters of multimodal MRI were measured and compared between the patients in the GBM and atypical PCNSL groups, and those parameters showing a significant difference (p < 0.05) between patients in the GBM and atypical PCNSL groups were used to develop one-parameters, unimodality, and bimodality models. We evaluated the efficiency of different models in distinguishing GBM from atypical PCNSL by performing receiver operating characteristic analysis (ROC).

Results

Atypical PCNSL had lower minimum apparent diffusion coefficient (ADC_min), mean ADC (ADC_mean), relative ADC (rADC), mean relative cerebral blood volume (rCBV_mean), maximum rCBV (rCBV_max), fractional anisotropy (FA), axial diffusion coefficient (DA) and radial diffusion coefficient (DR) values and higher choline/creatine (Cho/Cr) and lipid/creatine (Lip/Cr) ratios than GBM (all p < 0.05). The rCBV_max, DTI and DSC + DTI data were optimal models of single-parameter, unimodality and bimodality for differentiation of GBM from atypical PCNSL, yielding areas under the curves (AUCs) of 0.905, 0.954, and 0.992, respectively.

Conclusions

Models of single-parameter, unimodality and bimodality based on muti multiparameter functional MRI may help to discriminate GBM from atypical PCNSL.

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.

Nanomedicine Applications in Treatment of Primary Central Nervous System Lymphoma: Current State of the Art

Primary central nervous system lymphoma (PCNSL) is a rare but highly aggressive subtype of extra nodal non-Hodgkin lymphoma (NHL), which is confined in the central nervous system (CNS). Despite recent advancements in treatment options, the overall prognosis of PCNSL remains poor. Among many unfavorable factors affecting efficacy, inadequate drug delivery into the CNS is still the thorniest challenge. Blood-brain barrier (BBB) constitutes a significant impediment, restricting entry of most therapeutics to the brain. Nanotechnology has offered great promise for brain diseases, as various nano-based drug delivery systems (NDDSs) have been developed for delivery of theranostic agents in to the CNS. These drug delivery systems possess significant advantages, including good feasibility, reliable safety profile, excellent BBB penetration and potent antitumor effects. As for treatment of PCNSL, numerous well-developed BBB-crossing nano-based strategies can be applied with proper modifications and improvements. Some exquisitely designed NDDSs specific for PCNSL have shown great potential. In this review, we provide a summary on current status of diagnosis and treatment of PCNSL, followed by an overview of BBB-crossing strategies applied in management of PCNSL, both novel and wellestablished. Finally, challenges and future perspectives in this field are also discussed.

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.

Angiopep-2 conjugated nanoparticles loaded with doxorubicin for the treatment of primary central nervous system lymphoma

Primary central nervous system lymphoma (PCNSL) is a rare brain tumor. Its therapeutic efficacy is much lower than that of traditional lymphoma, largely due to the presence of the blood-brain barrier (BBB), which hinders the effective drug delivery and deposition on the disease site. Angiopep-2 (ANG) can target low-density lipoprotein receptor-related protein (LRP) on the surface of brain capillary endothelial cells (BCECs) and exhibits high BBB transport capability. In this study, we designed an ANG conjugated poly(ethylene glycol)-b-poly(ε-caprolactone) (PEG-b-PCL) (APP) nanoparticle to deliver doxorubicin (DOX) for the treatment of PCNSL. Our data indicated that the targeted APP nanoparticles showed significantly increased cellular uptake by BCECs compared with the control nanoparticles. In the intracranial SU-DHL-2-LUC lymphoma xenograft mice model, APP enhanced drug deposition in tumor tissues, and DOX-loaded APP (APP@DOX) exhibited a better therapeutic effect than free DOX and nontargeted PP@DOX, which significantly prolonged the survival time of mice.