Role of Magnetic Resonance Imaging (MRI) in grading gliomas comparable with pathology: A cross-sectional study from Syria

Haydar, Nisreen; Alyousef, Khatoun; Alanan, Usama; Issa, Rana; Baddour, Fawaz; Alshehabi, Zuheir; Al-janabi, Moatasem Hussein

doi:10.1016/j.amsu.2022.104679

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Other studies in Syria and 2012 reported varying sensitivity and specificity values, reinforcing the importance of individualized assessment. Our findings emphasize MR perfusion's high sensitivity and specificity in glioma identification, advocating its use as a first-line investigation in suspected cases and highlighting its efficacy in differentiating between low-grade and high-grade tumors (26)(27).…”

Section: Discussionsupporting

confidence: 53%

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Hameed,

Shafqat,

Hameed

et al. 2023

Pak J Public Health

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Background: Gliomas represent the most prevalent type of primary brain tumor and are associated with a significant mortality rate. The utilization of MR cerebral perfusion proves effective in distinguishing between low-grade and high-grade gliomas, surpassing the capabilities of routine MRI. This distinction allows for the timely initiation of surgical intervention. We aimed to document the diagnostic accuracy of MR cerebral perfusion against the gold standard of lab-based histopathology. Methods: We conducted this cross-sectional validation study over a duration of six months in the outpatient radiology department of a major public sector hospital of Rawalpindi, Pakistan. All patients had MR cerebral perfusion Scan of brain in order to differentiate low grade and high grade glioma. Quantitative rCBV analysis was carried out adopting a threshold value of 3, above this value the lesion was considered at a high grade. The results were written on specially designed proforma. Results: A total 230 patients were included in the study with mean age of 50.68+16.36 and there were 135 (58.7%) male and 95 (41.3%) were female. There were 122 (53.0%) patients presented with low grade glioma and 108 (47.0%) patients presented with high grade glioma. The sensitivity, specificity, PPV, NPV and diagnostic accuracy of MR cerebral perfusion to differentiate between low grade and high grade gliomas was 85.71%, 70.97%, 88.89%, 64.71% and 81.74% respectively. Conclusion: This study concludes that the MR perfusion reliably identifies higher-grade features in a tumor that otherwise may not appear on standard MRI scan.

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Section: Discussionsupporting

confidence: 53%

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Hameed,

Shafqat,

Hameed

et al. 2023

Pak J Public Health

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“…Conventional MRI is the diagnostic standard for patients with diffuse glioma and provides a detailed anatomical picture, as well as important qualitative tumor characteristics, such as contrast enhancement quality and the presence of necrosis or hemorrhage. Conventional brain MRI has been shown to be useful in predicting the pathological subtype [15] and even the molecular profile [16] of diffuse gliomas. The VASARI scoring system enables the quantification of tumor characteristics, making it easier to compare them between different groups [13].…”

Section: Discussionmentioning

confidence: 99%

“…In recent decades, many attempts have been made to accurately determine the type of diffuse brain glioma based on MRI features [11]- [13] or a combination of MRI and demographic characteristics [14]. In several studies, conventional MRI has been shown to predict both the pathological subtype [15] and even the molecular profile [16] of diffuse gliomas. To unify radiological reports and create a common vocabulary, the VASARI scoring system has been developed for a detailed description of brain gliomas (https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project), and its use has already shown promising results [13], [17].…”

Section: Introductionmentioning

confidence: 99%

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

Filimonova,

Pashkov,

Borisov

et al. 2023

Preprint

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Purpose Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique alone or in combination with other quantitative MRI sequences to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors. Methods Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT, arterial spin labeling (ASL), and diffusion-weighted imaging sequences. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables. Results We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas, indicating that the model performs better at detecting patients with glioblastoma rather than astrocytomas. Conclusion The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

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“…Brain gliomas (BG) are primary malignant tumour diseases threatening a considerable fraction of the human population [1]. The BG is divided into high‐grade gliomas (HGG) and low‐grade gliomas (LGG) based on a pathological evaluation, with a survival prognosis not exceeding one year for HGG.…”

Section: Introductionmentioning

confidence: 99%

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Yin

Gao

Gong

et al. 2023

IET Image Processing

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Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

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Role of Magnetic Resonance Imaging (MRI) in grading gliomas comparable with pathology: A cross-sectional study from Syria

Cited by 6 publications

References 9 publications

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Utilizing the Amide Proton Transfer Technique to Characterize Diffuse Gliomas Based on the WHO 2021 Classification of CNS Tumors

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

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