Perfusion imaging with arterial spin labeling (ASL)–MRI predicts malignant progression in low‑grade (WHO grade II) gliomas

Flies, C M; Snijders, Tom J.; Seeters, Tom van; Smits, Marion; Vos, Filip Y.F. De; Hendrikse, Jeroen; Dankbaar, Jan Willem

doi:10.1007/s00234-021-02737-4

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…Finally, ASL can be used for prognosis prediction of patients with glioblastoma, as low‐perfused gliomas appear to be associated with longer event‐free and OS 64 . Moreover, malignant progression in patients with grade 2 glioma can be predicted using ASL 67 …”

Section: Resultsmentioning

confidence: 99%

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Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Hirschler

Sollmann

Schmitz-Abecassis

et al. 2023

Magnetic Resonance Imaging

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Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast‐enhanced MRI, arterial spin labeling, diffusion‐weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility‐weighted imaging, MRI‐PET, MR elastography, and MR‐based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2

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Section: Resultsmentioning

confidence: 99%

“…64 Moreover, malignant progression in patients with grade 2 glioma can be predicted using ASL. 67 VALIDATION. The 2015 ISMRM Perfusion Study Group ASL recommendations resulted from a major effort to standardize ASL for clinical applications in the brain.…”

Section: Aslmentioning

confidence: 99%

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Hirschler

Sollmann

Schmitz-Abecassis

et al. 2023

Magnetic Resonance Imaging

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“…Future studies should focus on developing ML/DL models by combining different data sources (i.e., clinical, radiomic, semantic and PWI/DWI), which are correlated and may provide complementary information [ 95 ] for improving the clinical decision-making tasks [ 96 ]. Moreover, given the proven importance of quantitative techniques such as PWI and DWI, future ML/DL models should leverage the wealth of data provided by novel and more refined diffusion and perfusion techniques [ 97 , 98 , 99 , 100 , 101 ], potentially also including information upon cerebral metabolism [ 99 , 102 ]. By integrating all these data into a single multimodal model, further improvements in performance could be achieved.…”

Section: Discussionmentioning

confidence: 99%

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

et al. 2022

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Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

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“…Besides categorization into LGG and HGG, ASL-derived tumor perfusion has been associated with multiple markers that can impact treatment decision making and survival, including isocitrate dehydrogenase status, methylguanine-DNA methyltransferase promoter methylation, p53 status, as well as vascular endothelial growth factor expression and tumor microvascular density [94][95][96][97]. Furthermore, it has also been suggested that malignant progression within 12 months could be predicted with ASL-based perfusion imaging in patients with LGG, with a sensitivity of 73 %, specificity of 82 %, and odds ratio of 12 [98].…”

Section: Gliomasmentioning

confidence: 99%

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics – Methodological Overview and Use Cases

Sollmann,

Hoffmann,

Schramm

et al. 2023

Rofo

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Background Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF). Method This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine). Results ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels. Conclusion Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring. Key Points:

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Perfusion imaging with arterial spin labeling (ASL)–MRI predicts malignant progression in low‑grade (WHO grade II) gliomas

Cited by 10 publications

References 19 publications

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics – Methodological Overview and Use Cases

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