Differentiation between Glioblastoma and Primary Central Nervous System Lymphoma Using Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Comparison Study of the Manual versus Semiautomatic Segmentation Method

Kim, Ye Eun; Choi, Seung Hong; Lee, Sang Kun; Kim, Tae Min; Park, Shin Young; Kim, Il Han

doi:10.13104/imri.2017.21.1.9

Cited by 6 publications

(5 citation statements)

References 35 publications

(81 reference statements)

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“… 24 , 25 , 26 , 27 Of particular significance is dynamic susceptibility contrast (DSC) perfusion MRI, which enables the visualization of vascular characteristics, including vascular density and permeability, and is proving to be valuable for brain tumor diagnosis. 9 , 10 , 18 , 20 , 22 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 …”

Section: Introductionmentioning

confidence: 99%

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Garcia-Ruiz,

Pons-Escoda,

Grussu

et al. 2024

Cell Reports Medicine

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Section: Introductionmentioning

confidence: 99%

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Garcia-Ruiz,

Pons-Escoda,

Grussu

et al. 2024

Cell Reports Medicine

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“…DSC is commonly used to evaluate and differentiate types of brain tumors (7) and is a fundamental technique for brain tumor follow-up (8)(9)(10). The DSC method provides many parameters related to perfusion, not only relative blood flow (rBF) and relative blood volume (rBV) in the cerebrum, but also the mean transit time (MTT) and leakage coefficient (K2), which reflect physiological conditions (7) such as angiogenesis and vascular density (8)(9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

Correlation and Characteristics of Intravoxel Incoherent Motion and Arterial Spin Labeling Techniques Versus Multiple Parameters Obtained on Dynamic Susceptibility Contrast Perfusion MRI for Brain Tumors

et al. 2019

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Purpose : To compare data on brain tumors derived from intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) imaging with multiple parameters obtained on dynamic susceptibility contrast (DSC) perfusion MRI and to clarify the characteristics of IVIM and ASL perfusion data from the viewpoint of cerebral blood flow (CBF) analysis. Methods : ASL-CBF and IVIM techniques as well as DSC examination were performed in 24 patients with brain tumors. The IVIM data were analyzed with the two models. The relative blood flow (rBF), relative blood volume (rBV) corrected relative blood volume (crBV), mean transit time (MTT), and leakage coefficient (K2) were obtained from the DSC MRI data. Results : The ASL-CBF had the same tendency as the perfusion parameters derived from the DSC data, but the permeability from the vessels had less of an effect on the ASL-CBF. The diffusion coefficient of the fast component on IVIM contained more information on permeability than the f value. Conclusion : ASL-CBF is more suitable for the evaluation of perfusion in brain tumors than IVIM parameters. ASL-CBF and IVIM techniques should be carefully selected and the biological significance of each parameter should be understood for the correct comprehension of the pathological status of brain tumors. J. Med. Invest. 66 : 308-313, August, 2019

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“…Brain metastases and glioblastoma are the most common malignant brain tumors in adults (27)(28)(29). In conventional MRI, differentiating single brain metastases from a glioblastoma by radiologists remains a challenge because both can manifest as a ring-enhancing mass with internal necrosis and peritumoral edema.…”

Section: Differential Diagnosis With Glioblastomamentioning

confidence: 99%

Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications

Park

Lee

Ahn

et al. 2021

Investig Magn Reson Imaging

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Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.

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Differentiation between Glioblastoma and Primary Central Nervous System Lymphoma Using Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Comparison Study of the Manual versus Semiautomatic Segmentation Method

Cited by 6 publications

References 35 publications

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Correlation and Characteristics of Intravoxel Incoherent Motion and Arterial Spin Labeling Techniques Versus Multiple Parameters Obtained on Dynamic Susceptibility Contrast Perfusion MRI for Brain Tumors

Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications

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