scholarly journals Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

2020 ◽  
Vol 33 (5) ◽  
pp. 428-436
Author(s):  
Mehrsad Mehrnahad ◽  
Sara Rostami ◽  
Farnaz Kimia ◽  
Reza Kord ◽  
Morteza Sanei Taheri ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Diffusion Coefficient ◽  
Glioblastoma Multiforme ◽  
Apparent Diffusion Coefficient ◽  
Central Nervous System Lymphoma ◽  
Second Order ◽  
Regions Of Interest ◽  
Apparent Diffusion ◽  
Histogram Feature

Purpose The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients. Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. Results Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group ( P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1–99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9–92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group ( P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2–100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3–95.8%). Conclusions Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

Apparent diffusion coefficient as a valuable quantitative parameter for predicting clinical outcomes in patients with newly diagnosed primary CNS lymphoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14532-e14532
Author(s):  
Dong Won Baek ◽  
Soo Jung Lee ◽  
Byung Woog Kang ◽  
Joon Ho Moon ◽  
Jong Gwang Kim ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Group Performance ◽  
Cns Lymphoma ◽  
Newly Diagnosed ◽  
Term Survival ◽  
Apparent Diffusion ◽  
Ecog Ps

e14532 Background: Primary central nervous system lymphoma (PCNSL) is a rare subtype of non-Hodgkin lymphoma (NHL). Although, there are various treatment options for PCNSL which include methotrexate based chemotherapy and whole brain radiotherapy (WBRT), half of the patients who initially achieved complete response experience disease relapse. Accordingly, we attempted to identify novel prognostic factors using MRI in patients with newly diagnosed PCNSL. Methods: We retrospectively evaluated 672 patients who were diagnosed with central nervous system (CNS) cancers between January 2011 and May 2018. Enrollment criteria were i) pathologic diagnosis of CNS lymphoma, ii) no evidence of systemic involvement, iii) no evidence of human immunodeficiency virus-1 infection or other immunodeficiencies, and iv) available magnetic resonance imaging (MRI) examinations at diagnosis. Fifty-two patients met these criteria and were enrolled. Results: Patients with low apparent diffusion coefficient (ADC) showed inferior overall survival (OS) compared to those with high ADC. Patients with a hyperintense signal on T2-weighted image and homogenous enhancement showed better failure-free survival (FFS), while patients with low ADC and necrosis showed poor FFS. In the multivariate survival analysis, old age ( > 60) (hazard ratio (HR) 20.372, p= 0.001), Eastern Cooperative Oncology Group performance status ((ECOG PS) ≥ 2 (HR 10.429, p < 0.001), higher levels of lactate dehydrogenase (LDH) (HR 7.408, p= 0.001), and low ADC (HR 0.273, p= 0.009) were associated with inferior OS, while ECOG PS ≥ 2 (HR 10.319, p= 0.021), presence of necrosis (HR 6.372, p= 0.008), and low ADC (HR 0.226, p= 0.020) were unfavorable factors for FFS. Conclusions: We conclude that ADC, a characteristic MRI finding, had significant prognostic value for long-term survival in our study of patients with newly diagnosed PCNSL Specifically, low ADC was an independent unfavorable prognostic factor, suggesting that ADC measurements through non-invasive MRI can improve the current prognostic scoring system.

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

2020 ◽  
Vol 13 (12) ◽  
Author(s):  
Nguyen Duy Hung ◽  
Nguyen Minh Duc ◽  
Ta Hong Nhung ◽  
Le Thanh Dung ◽  
Bui Van Giang ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Blood Volume ◽  
Cerebral Blood Volume ◽  
Proliferation Index ◽  
Cns Lymphoma ◽  
Ki 67 ◽  
Apparent Diffusion

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.

scholarly journals Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Novak ◽  
Niloufar Zarinabad ◽  
Heather Rose ◽  
Theodoros Arvanitis ◽  
Lesley MacPherson ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Posterior Fossa ◽  
Brain Tumours ◽  
Diffusion Weighted Imaging ◽  
Regions Of Interest ◽  
Apparent Diffusion Coefficients ◽  
Diffusion Weighted ◽  
Apparent Diffusion ◽  
Paediatric Brain Tumours

AbstractTo determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10−3 mm2 s−1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

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