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

Öztürk, Kerem; Soylu, Esra; Cayci, Zuzan

doi:10.1016/j.ejrad.2021.109784

Cited by 10 publications

(13 citation statements)

References 37 publications

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“…Another study (93) showed that the combination of mean and maximum CBV, mean and maximum selective ADC, and rCBV mean could obtain 100% accuracy for discrimination between 37 PCNSLs and 37 GBMs. A corporation analysis of diffusion and susceptibility also showed a good performance (18). Ozturk et al (18) combined rSWI with rADC mean to differentiate 31 B-cell type PCNSLs with BCL2 and MYC rearrangements from 57 atypical GBMs without visible necrosis.…”

Section: Promising Combination Studies Of Multiple Analysesmentioning

confidence: 99%

“…A corporation analysis of diffusion and susceptibility also showed a good performance (18). Ozturk et al (18) combined rSWI with rADC mean to differentiate 31 B-cell type PCNSLs with BCL2 and MYC rearrangements from 57 atypical GBMs without visible necrosis. The combination improved the diagnostic performance to an AUC of 0.936.…”

Section: Promising Combination Studies Of Multiple Analysesmentioning

confidence: 99%

“…However, there are considerable overlapping appearances between PCNSL and HGG in atypical cases, which cause confusion in conventional MRI, especially in cases of immunodeficiency and after treatment. For example, GBM with homogeneous enhancement but no apparent necrosis may mimic PCNSL, while atypical PCNSL with heterogeneous enhancement and central necrosis may mimic GBM ( 18 ). However, advanced MRI techniques can provide more evidence for the differential diagnosis.…”

Section: Radiological Progress In Pcnsl and Hggmentioning

confidence: 99%

“…In addition, molecular types of tumors may affect ADC values. For example, MYC proto-oncogene bHLH transcription factor (MYC) and BCL2 apoptosis regulator (BCL2) gene rearrangements in CNS DLBCL lead to lower rADC min and rADC mean values, while isocitrate dehydrogenase [NADP(+)]1 (IDH1) mutation in GBM leads to higher values, when compared with wild-types ( 18 ). Methylation of the O(6)-methylguanine-DNA methyltransferase (MGMT) promoter in GBM may lead to higher ADC values with lower perfusion ( 26 ).…”

Section: Radiological Progress In Pcnsl and Hggmentioning

confidence: 99%

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Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review)

Cao

Zhang

et al. 2022

Oncol Rep

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Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy-based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.

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Section: Promising Combination Studies Of Multiple Analysesmentioning

confidence: 99%

Section: Promising Combination Studies Of Multiple Analysesmentioning

confidence: 99%

Section: Radiological Progress In Pcnsl and Hggmentioning

confidence: 99%

Section: Radiological Progress In Pcnsl and Hggmentioning

confidence: 99%

See 2 more Smart Citations

Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review)

Cao

Zhang

et al. 2022

Oncol Rep

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“…Although there are a few semantic MR imaging features that help the radiologist to differentiate PCNSL from GBM ( 3 ), these features are subjective and dependant on the expertise and the experience of the radiologist with a resultant dependence on the gold standard histopathology of the tumour specimen ( 4 ). Certain special MRI sequences such as Diffusion Weighted Imaging (DWI), MR spectroscopy (MRS) may complement the semantic features ( 5 ), and could be useful in differentiating the two tumours but these special MR protocols are resource intense and their use is limited due to lack of widespread availability and associated cost escalations have practise implications in high throughput cancer centres.…”

Section: Introductionmentioning

confidence: 99%

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

et al. 2022

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BackgroundGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM.MethodsThe authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.ResultsTen articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.ConclusionsExtensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.

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AdvancedMRTechniques for Preoperative Glioma Characterization: Part 2

Hangel

Schmitz-Abecassis

Sollmann

et al. 2023

Magnetic Resonance Imaging

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

Cited by 10 publications

References 37 publications

Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review)

Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review)

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

AdvancedMRTechniques for Preoperative Glioma Characterization: Part 2

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