ArticleEuropean radiology2017
Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update.Journal of neuro-oncology · 2020Pooled it
- Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis.Cancer medicine · 2019Pooled it
- Pooled it
- Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.BMC medical imaging · 2026Article
- Tumor Habitats Based on Multiparametric MRI Distinguish Atypical Glioblastoma From Primary Central Nervous System Lymphoma: Imaging-Pathologic Correlation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Multiparametric MRI for differential diagnosis of primary central nervous system lymphoma and atypical glioblastoma: an analysis incorporating DWI, DCE-MRI, and contrast agent preload DSC-PWI.BMC medical imaging · 2025Article
- Histogram analysis based on DTI and NODDI for differentiating atypical high-grade glioma from primary central nervous system lymphoma.Frontiers in neurology · 2025Article
- Article
- Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging.Frontiers in oncology · 2024Review
- Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling.European radiology · 2023Article
- A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis.Cancers · 2022Review
- Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.Cancers · 2022Review
- Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study.Frontiers in oncology · 2022Article
- Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet.Neurophotonics · 2022Article
- Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features.Frontiers in oncology · 2022Article
- Machine Learning and Deep Learning CT-Based Models for Predicting the Primary Central Nervous System Lymphoma and Glioma Types: A Multicenter Retrospective Study.Frontiers in neurology · 2022Article
- Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors.BMC medical imaging · 2021Article
- Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.European radiology · 2021Article
- Primary central nervous system lymphoma: advances in MRI and PET imaging.Annals of lymphoma · 2021Article
- LCK expression is a potential biomarker for distinguishing primary central nervous system lymphoma from glioblastoma multiforme.FEBS open bio · 2020Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the ability of the initial area under the curve (IAUC) derived from dynamic contrast-enhanced MR imaging (DCE-MRI) and apparent diffusion coefficient (ADC) in differentiating between primary central nervous system lymphoma (PCNSL) and atypical glioblastoma (GBM).
methodsWe retrospectively identified 19 patients with atypical GBM (less than 13 % necrosis of the enhancing tumour), and 23 patients with PCNSL. The histogram parameters of IAUC at 30, 60, 90 s (IAUC30, IAUC60, and IAUC90), and ADC were compared between PCNSL and GBM. The diagnostic performances and added values of the IAUC and ADC for differentiating between PCNSL and GBM were evaluated. Interobserver agreement was assessed via intraclass correlation coefficient (ICC).
resultsThe IAUC and ADC parameters were higher in GBM than in PCNSL. The 90th percentile (p90) of IAUC30 and 10th percentile (p10) of ADC showed the best diagnostic performance. Adding p90 of IAUC30 to p10 of ADC improved the differentiation between PCNSL and GBM (area under the ROC curve [AUC] = 0.886), compared to IAUC30 or ADC alone (AUC = 0.789 and 0.744; P < 0.05 for all). The ICC was 0.96 for p90 of IAUC30.
conclusionsThe IAUC may be a useful parameter together with ADC for differentiating between PCNSL and atypical GBM. KEY POINTS: • High reproducibility is essential for practical implementation of advanced MRI parameters. • IAUC and ADC are highly reproducible parameters. • IAUC values were higher in atypical GBM than in PCNSL. • Adding IAUC to ADC improved the differentiation between PCNSL and GBM. • IAUC with ADC are useful for differentiating PCNSL from GBM.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.