Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors

Arisawa, Atsuko; Watanabe, Yoshiyuki; Tanaka, Hisashi; Takahashi, Hiroto; Matsuo, Chisato; Fujiwara, Takuya; Fujiwara, Masahiro; Fujimoto, Yasunori; Tomiyama, Noriyuki

doi:10.1007/s00234-018-2024-2

Cited by 15 publications

(12 citation statements)

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“…LGGs and HGGs of 94.4% and 94% respectively when T1-weighted before and 453 after contrast-enhanced images were studied, and 96.5% and 97% when they studied 454 T2-weighted and FLAIR images. Therefore, in this work conventional MRI (T 1Gd and 455 T 2 contrasts) was studied, while others have analyzed advanced MRI or a combination 456 of both [5,[21][22][23][24][51][52][53][54]. The model was created from a simple mathematical method (a 457 multiple linear regression), in comparison to others in which mathematical tools of 458 higher complexity were utilized [7,[52][53][54]].…”

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confidence: 99%

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Suárez-García

Hernández-López

Moreno-Barbosa

et al. 2020

Preprint

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11Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and 12 prognosis of patients. The purpose of this work was to develop a low cost and easy to 13 implement classification model which distinguishes low grade gliomas (LGGs) from high 14 grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. 15Different combinations between MRI contrasts (T 1Gd and T 2 ) and one segmented 16 glioma region (necrotic and non-enhancing tumor core (NCR/NET)) were studied. 17 Texture features obtained from the Gray Level Size Zone Matrix (GLSZM) were 18 calculated. An under-samplig method was proposed to divide the data into different 19 training subsets and subsequently extract complementary information for the creation 20 of distinct classification models. The sensitivity, specificity and accuracy of the models 21 were calculated. The best model was explicitly reported. The best model included only 22 three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 23 88.24% and 91.18% respectively. According to the features of the model, when the 24 NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in 25 the T 1Gd images and LGGs had a more heterogeneous texture than HGGs in the T 2 26 images. These novel results partially contrast with results from literature. The best 27 model proved to be useful for the classification of gliomas. Complementary results 28 showed that heterogeneity of gliomas depended on the studied MRI contrast. The 29 model presented stands out as a simple, low cost, easy to implement, reproducible and 30 highly accurate glioma classifier. What is more important, it should be accessible to 31 populations with reduced economic and scientific resources.32

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confidence: 99%

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Suárez-García

Hernández-López

Moreno-Barbosa

et al. 2020

Preprint

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“…Although ASL-perfusion is a relatively new method, it has already proved to be effective in diagnosis of cerebral gliomas. There is a number of studies establishing a high correlation between tumor blood ow derived from ASL-perfusion and DSC-perfusion which is known as the "gold standard" in perfusion studies [33,34].…”

Section: Discussionmentioning

confidence: 99%

ASL-Perfusion for Intrinsic Brain Tumor Diagnosis. Analysis of 253 Patients.

Баталов

Захарова

Pronin

et al. 2021

Preprint

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Purpose The aim of the study was to evaluate the role of pseudo-continuous ASL-perfusion (pCASL-perfusion) in preoperative assessing of cerebral glioma grades. Methods The study group consisted of 253 patients aged 7 to 78 years with supratentorial gliomas (65 had low-grade gliomas (LGG), 188 – high-grade gliomas (HGG)). Maximal tumor blood flow (maxTBF) in small ROIs (20 mm2 ± 10 mm2) were evaluated by subsequently normalized tumor blood flow (nTBF) calculation which was compared with normal appearing white matter of center semiovale of the contralateral hemisphere. Results TBF and nTBF values were significantly differed in HGG and LGG groups, as well as grade II and grade III gliomas; grade III and grade IV gliomas (p < 0.001). ASL-perfusion has demonstrated both high sensitivity and specificity in differentiating LGG and HGG, grade II and grade III gliomas, but low sensitivity and specificity in distinguishing grade III and grade IV gliomas. We did not observe a significant difference in TBF in astrocytomas and oligodendrogliomas. Conclusion Current results demonstrate that 3D pCASL-perfusion is an effective diagnostic tool for preoperative differentiation of low and high grade gliomas.

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“…They obtained an accuracy for classifying LGGs and HGGs of 94.4% and 94%, respectively, when T1-weighted images before and after contrast-enhancement were studied, and 96.5% and 97% when they studied T2-weighted and FLAIR images. Therefore, in this work conventional MRI (T 1Gd and T 2 contrasts) was studied, while others have analyzed advanced MRI or a combination of both [5,[21][22][23][24][51][52][53][54]. The model was created from a simple mathematical method (a multiple linear regression), in comparison to others in which mathematical tools of higher complexity were utilized [7,[52][53][54].…”

Section: Plos Onementioning

confidence: 99%

“…Some of them study conventional (anatomical) [7,20] or advanced MRI (perfusion or diffusion weighted imaging, spectroscopy, etc.) [5,[21][22][23]. They use qualitative, semiquantitative or quantitative variables, or a combination.…”

Section: Introductionmentioning

confidence: 99%

“…For classification models, this commonly consists of using training samples to create the model and testing samples to ratify its results. However, because the available databases tipically contain small samples, it is not always possible to validate the results [19,[21][22][23]. Moreover, many of the databases used in different studies are private, or were acquired using specific protocols, which does not always allow their results to be extrapolated or reproduced through independent studies.…”

Section: Introductionmentioning

confidence: 99%

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A simple model for glioma grading based on texture analysis applied to conventional brain MRI

et al. 2020

View full text Add to dashboard Cite

Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low-cost and easy-to-implement classification model which distinguishes low-grade gliomas (LGGs) from high-grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations of MRI contrasts (T 1Gd and T 2) and one segmented glioma region (necrotic and non-enhancing tumor core, NCR/NET) were studied. Texture features obtained from the gray level size zone matrix (GLSZM) were calculated. An under-sampling method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated, and the best model explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18%, respectively. According to the features of the model, when the NCR/ NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T 1Gd images, and LGGs had a more heterogeneous texture than HGGs in the T 2 images. These novel results partially contrast with results from the literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that the heterogeneity of gliomas depended on the MRI contrast studied. The chosen model stands out as a simple, low-cost, easy-to-implement, reproducible and highly accurate glioma classifier. Importantly, it should be accessible to populations with reduced economic and scientific resources.

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Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors

Abstract: DSC-MRI is superior to ASL for distinguishing high-grade from low-grade glioma. ASL could be an alternative evaluation method when DSC-MRI cannot be used, e.g., in patients with renal failure, those in whom repeated examination is required, and in children.

Cited by 15 publications

References 44 publications

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

ASL-Perfusion for Intrinsic Brain Tumor Diagnosis. Analysis of 253 Patients.

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

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