Usefulness of enhancement-perfusion mismatch in differentiation of CNS lymphomas from other enhancing malignant tumors of the brain

Predictors for the Differentiation between Glioblastoma, Primary Central Nervous System Lymphoma, and Metastasis in Patients with a Solitary Enhancing Intracranial Mass

Introduction  Differentiation between glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastasis is important in decision-making before surgery. However, these malignant brain tumors have overlapping features. This study aimed to identify predictors differentiating between GBM, PCNSL, and metastasis. Materials and Methods  Patients with a solitary intracranial enhancing tumor and a histopathological diagnosis of GBM, PCNSL, or metastasis were investigated. All patients with intracranial lymphoma had PCNSL without extracranial involvement. Demographic, clinical, and radiographic data were analyzed to determine their associations with the tumor types. Results  The predictors associated with GBM were functional impairment ( p  = 0.001), large tumor size ( p  < 0.001), irregular tumor margin ( p  < 0.001), heterogeneous contrast enhancement ( p  < 0.001), central necrosis ( p  < 0.001), intratumoral hemorrhage ( p  = 0.018), abnormal flow void ( p  < 0.001), and hypodensity component on noncontrast cranial computed tomography (CT) scan ( p  < 0.001). The predictors associated with PCNSL comprised functional impairment ( p  = 0.005), deep-seated tumor location ( p  = 0.006), homogeneous contrast enhancement ( p  < 0.001), absence of cystic appearance ( p  = 0.008), presence of hypointensity component on precontrast cranial T1-weighted magnetic resonance imaging (MRI; p  = 0.027), and presence of isodensity component on noncontrast cranial CT ( p  < 0.008). Finally, the predictors for metastasis were an infratentorial ( p  < 0.001) or extra-axial tumor location ( p  = 0.035), smooth tumor margin ( p  < 0.001), and presence of isointensity component on cranial fluid-attenuated inversion recovery MRI ( p  = 0.047). Conclusion  These predictors may be used to differentiate between GBM, PCNSL, and metastasis, and they are useful in clinical management.

Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System

BACKGROUND

Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited.

PURPOSE

To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin.

STUDY TYPE

Retrospective.

POPULATION

A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43).

FIELD STRENGTH/SEQUENCE

A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts.

STATISTICAL TESTS

Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC).

RESULTS

Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively).

DATA CONCLUSION

The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

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.

The Real-World status and risk factors for a poor prognosis in elderly patients with primary central nervous system malignant lymphomas: a multicenter, retrospective cohort study of the Tohoku Brain Tumor Study Group

BACKGROUND

Elderly patients with primary central nervous system malignant lymphoma (EL-PCNSL) may not be given sufficient treatment due to their poor pre-treatment Karnofsky Performance Status (KPS) and comorbidities. Therefore, a retrospective, cohort study was performed to evaluate risk factors associated with a poor prognosis of EL-PCNSL in the Tohoku Brain Tumor Study Group.

METHODS

Patients aged ≥ 71 years with PCNSL were enrolled from eight centers. Univariate analysis was performed with the log-rank test. A Cox proportional hazards model was used for multivariate analysis.

RESULTS

Three of the total 142 cases received best supportive care (BSC). Treatment was given to 30 cases without a pathological diagnosis, 3 cases with cerebrospinal fluid (CSF) cytology, and 100 cases with a pathological diagnosis. After confirmation of no differences in progression-free survival (PFS) and overall survival (OS) between the group treated without pathology and the groups diagnosed by pathology or CSF cytology and between median age ≥ 76 years and < 76 years, a total of 133 patients were studied. The median pre-treatment KPS was 50%. Median PFS and median OS were 16 and 24 months, respectively. Risk factors associated with poor prognosis on Cox proportional hazards model analysis were pre-treatment cardiovascular disease and central nervous system disease comorbidities, post-treatment pneumonia and other infections, and the absence of radiotherapy or chemotherapy.

CONCLUSIONS

Pre-treatment comorbidities and post-treatment complications would affect the prognosis. Radiation and chemotherapy were found to be effective, but no conclusions could be drawn regarding the appropriate content of chemotherapy and whether additional radiotherapy should be used.

Application of intraoperative B-mode ultrasound and shear wave elastography for glioma grading

Background

To evaluate the value of intraoperative B-mode ultrasound and shear wave elastography (SWE) in differentiating low-grade and high-grade gliomas.

Methods

A total of 172 patients with glioma were examined by B-mode ultrasound to obtain a tumor sonogram. Intraoperative SWE was performed on 52 patients to obtain Young's modulus values of peritumor tissue and tumor tissue, and the differences in conventional B-mode signs and Young's modulus values of gliomas of different grades were then compared. The diagnostic performance of SWE in glioma grading was assessed by receiver operating characteristic (ROC) curve analysis, and the intra- and interobserver reliability of SWE was analyzed by the intraclass correlation coefficient (ICC).

Results

For B-mode ultrasound, patient age, cystic degeneration, and peritumor edema were independent risk factors for high-grade glioma (P<0.05, OR >1). For SWE, Young's modulus values of peritumor tissue, low-grade glioma, and high-grade glioma tissues were 8.20 (7.50, 9.70) kPa, 19.65 (15.30, 24.75) kPa, and 9.55 (8.50, 13.80) kPa, respectively. The area under the ROC curve for the diagnosis of high-grade glioma by SWE was 0.859 (95% CI: 0.758-0.961, P<0.05), and the optimal cutoff value was 12.1 kPa, with 89.3% sensitivity and 75.0% specificity. The intra- and interobserver reliability of SWE in grading gliomas was excellent, with ICCs ranging from 0.921 to 0.965.

Conclusions

High-grade glioma is associated with significantly more severe necrotic cystic degeneration and peritumoral edema on B-mode ultrasound and lower stiffness on SWE. Further, SWE exhibits excellent intra- and interobserver reliability. Intraoperative B-mode ultrasound combined with SWE helps differentiate different grades of gliomas.

Differentiation of Primary Central Nervous System Lymphoma and High-Grade Glioma with Dynamic Susceptibility Contrast-Derived Metrics: Pilot Study

OBJECTIVE

Preoperative differentiation of lymphoma from other aggressive intracranial neoplasms is important as the surgical and adjuvant therapy may be fundamentally different between the 2 types of tumors. The purpose of this study was to assess the ability of the dynamic susceptibility contrast-derived metrics, percentage signal recovery (PSR) ratio, and relative cerebral blood volume (rCBV) to distinguish between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG).

METHODS

Twenty-six patients (15 with HGG and 11 with PCNSL) with histologically confirmed diagnoses were retrospectively analyzed. Mean PSR and rCBV were calculated from dynamic susceptibility contrast imaging. The 2 groups were compared using an independent samples t-test. Receiver operating characteristic analyses were performed to determine the area under the curve and identify threshold values to differentiate PCNSL from GBM.

RESULTS

Both rCBV and PSR values were significantly different, at both the group level and subject level, between the PCNSL and HGG patients. The mean rCBV was significantly lower in PCNSL (1.38 ± 0.64) compared with HGG (5.19 ± 2.21, df = 11.24, P < 0.001). The mean PSR ratio was significantly higher in PCNSL (1.04 ± 0.11) compared with HGG (0.72 ± 0.16, df = 17.23, P < 0.001). An rCBV threshold value of 2.67 provided a 100% sensitivity and 100% specificity (area under the curve 1.0) for differentiating PCNSL from HGG. A PSR ratio threshold value of 0.9 was 100% sensitive and 90.91% specific for differentiating PCNSL from HGG.

CONCLUSIONS

The findings of our study show that rCBV and PSR ratio are different in HGG and PCNSL at both the group level and subject level. Incorporation of perfusion in routine magnetic resonance imaging of contrast-enhancing lesions can have a significant impact on patient management.

The Correlation Between Apparent Diffusion Coefficient (ADC) and Relative Cerebral Blood Volume (rCBV) with Ki-67 Expression in Central Nervous System Lymphoma

Background: Central nervous system (CNS) lymphoma presents as the dense infiltration of tumor cells in the perivascular space and blood-brain barrier disruption, on histopathological examination. The Ki-67 expression has been significantly correlated with tumor proliferation and is considered to be a prognostic factor. Objectives: This study aimed at analyzing the correlations among the apparent diffusion coefficient (ADC), the relative cerebral blood volume (rCBV), and the Ki-67 proliferation index in CNS lymphoma. Methods: From August 2019 to March 2020, 26 patients (14 men and 12 women) who underwent biopsy or surgery and were histologically confirmed as CNS lymphoma were included in this retrospective study. Diffusion and perfusion acquisitions were performed in 26 and 10 examinations, respectively. The Ki-67 proliferation index was available for all cases. Results: The mean tADC, rADC, and rCBV values were 0.61 ± 0.12 × 10-3 mm2/s, 0.73 ± 0.14, and 1.1 ± 0.32, respectively. Negative correlations were identified between both tADC and rADC and the Ki-67 proliferation index (r = -0.656, P < 0.01 and r = -0.540, P < 0.01, respectively). No significant correlations were found between rCBV values and the Ki-67 proliferation index, between rCBV and rADC, or between rCBV and tADC. Conclusions: tADC and rADC values can be used as noninvasive indicators to predict cell proliferation in CNS lymphoma.

Performance of diffusion and perfusion MRI in evaluating primary central nervous system lymphomas of different locations

Background

Diffusion and perfusion MRI can invasively define physical properties and angiogenic features of tumors, and guide the individual treatment. The purpose of this study was to investigate whether the diffusion and perfusion MRI parameters of primary central nervous system lymphomas (PCNSLs) are related to the tumor locations.

Methods

We retrospectively reviewed the diffusion, perfusion, and conventional MRI of 68 patients with PCNSLs at different locations (group 1: cortical gray matter, group 2: white matter, group 3: deep gray matter). Relative maximum cerebral blood volume (rCBV_max) from perfusion MRI, minimum apparent diffusion coefficients (ADC_min) from DWI of each group were calculated and compared by one-way ANOVA test. In addition, we compared the mean apparent diffusion coefficients (ADC_mean) in three different regions of control group.

Results

The rCBV_max of PCNSLs yielded the lowest value in the white matter group, and the highest value in the cortical gray matter group (P < 0.001). However, the ADC_min of each subgroup was not statistically different. The ADC_mean of each subgroup in control group was not statistically different.

Conclusion

Our study confirms that rCBV_max of PCNSLs are related to the tumor location, and provide simple but effective information for guiding the clinical practice of PCNSLs.

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.

Grading of oligodendroglial tumors of the brain with apparent diffusion coefficient, magnetic resonance spectroscopy, and dynamic susceptibility contrast imaging

Purpose We explored whether advanced magnetic resonance (MR) imaging techniques could grade oligodendrogliomas. Methods Forty patients (age 9-61 years) with oligodendroglial tumors were selected. There were 23 patients with World Health Organization grade II (group 1) and 17 patients with grade III (group 2) tumors. Apparent diffusion coefficient (ADC) maps were calculated by b values of 0 and 1000 s/mm². Dynamic susceptibility contrast (DSC) images were obtained during the first pass of a bolus of gadolinium-based contrast. These data were post-processed and cerebral blood volume (CBV) maps and permeability (PS) were calculated. MR spectroscopy was acquired after drawing a region of interest on the tumor using two-dimensional chemical shift imaging. Statistical analysis was performed using SPSS software. Results When the rPS_max was combined with the rCBV_max, there was a significant difference between the two groups ( p ≤ 0.03) with area under the curve of 0.742 (95% CI: 0.412-0.904). rCBV, rADC, choline/creatine, and choline/NAA alone were able to differentiate between the two groups; however, they did not show any statistical difference with p values of ≤ 0.121, ≤ 0.722, and ≤ 0.582, respectively. A CBV PS product threshold of 0.53 provided a sensitivity of 80% and a specificity of 83.3% in detection of grade III tumors. Conclusion Combined rCBV_max and rPS_max can be utilized to grade oligodendrogliomas. ADC values, relative cerebral blood volume (rCBV), and MR spectroscopy alone can be utilized to differentiate between the two groups of oligodendrogliomas but without statistical significance.