Characterization of Skull Base Lesions Using Pseudo-Continuous Arterial Spin Labeling

Arterial spin labeling (ASL) is a powerful noncontrast magnetic resonance imaging (MRI) technique that enables quantitative evaluation of brain perfusion. To optimize the clinical and research utilization of ASL, radiologists and physicists must understand the technical considerations and age-related variations in normal and disease states. We discuss advanced applications of ASL across the lifespan, with example cases from children and adults covering a wide variety of pathologies. Through literature review and illustrated clinical cases, we highlight the subtleties as well as pitfalls of ASL interpretation. First, we review basic physical principles, techniques, and artifacts. This is followed by a discussion of normal perfusion variants based on age and physiology. The three major categories of perfusion abnormalities-hypoperfusion, hyperperfusion, and mixed patterns-are covered with an emphasis on clinical interpretation and relationship to the disease process. Major etiologies of hypoperfusion include large artery, small artery, and venous disease; other vascular conditions; global hypoxic-ischemic injury; and neurodegeneration. Hyperperfusion is characteristic of vascular malformations and tumors. Mixed perfusion patterns can be seen with epilepsy, migraine, trauma, infection/inflammation, and toxicmetabolic encephalopathy. Level of Evidence: 4 Technical Efficacy Stage: 3

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“… 87 Sinonasal, calvarial, and skull base hypervascular lesions such as paraganglioma (Fig. 10i) are readily identified by ASL 93 …”

Section: Hyperperfusion Patternsmentioning

confidence: 99%

Arterial Spin Labeling Applications in Pediatric and Adult Neurologic Disorders

Bambach

Smith

Morris

et al. 2020

Magnetic Resonance Imaging

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“…Tumor blood flow (TBF) in GH-producing PitNETs/pituitary adenomas measured by ASL is reported to decrease after octreotide treatment, reflecting the antiangiogenic effect of octreotide [ 51 ]. TBF measured by ASL has also been reported to be significantly higher in suprasellar meningiomas (absolute median TBF; 172.95 mL/100 g/min) than in PitNETs/pituitary adenomas (absolute median TBF; 34.57 mL/100 g/min) [ 52 ].…”

Section: Pituitary Neuroendocrine Tumor (Pitnet)/pituitary Adenomamentioning

confidence: 99%

Imaging of pituitary tumors: an update with the 5th WHO Classifications—part 1. Pituitary neuroendocrine tumor (PitNET)/pituitary adenoma

Tsukamoto¹,

Miki²

2023

Jpn J Radiol

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The pituitary gland is the body’s master gland of the endocrine glands. Although it is a small organ, many types of tumors can develop within it. The recently revised fifth edition of the World Health Organization (WHO) classifications (2021 World Health Organization Classification of Central Nervous System Tumors and 2022 World Health Organization Classification of Endocrine and Neuroendocrine Tumors) revealed significant changes to the classification of pituitary adenomas, the most common type of pituitary gland tumor. This change categorized pituitary adenomas as neuroendocrine tumors and proposed the name to be revised to pituitary neuroendocrine tumor (PitNET). The International Classification of Diseases for Oncology behavior code for this tumor was previously “0” for benign tumor. In contrast, the fifth edition WHO classification has changed this code to “3” for primary malignant tumors as same to neuroendocrine tumor in other organs. Because the WHO classification made an important and significant change in the fundamental concept of the disease, in this paper, we will discuss the imaging diagnosis (magnetic resonance imaging, computed tomography, and positron emission tomography) of PitNET/pituitary adenoma in detail, considering these revisions as per the latest version of the WHO classification.

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“…In this regard, the value of perfusion MRI and arterial spin labelling (ASL) in the differentiation of certain dural metastases from meningioma has been previously indicated. 10,11 Of note, ASL has previously shown potential not only in the differentiation of meningioma radiological mimics such as vestibular schwannoma, 74,75 but also in meningioma grading 76,77 and the diagnosis of residual and recurrent meningioma. 78 One of the advantages of ASL is that it does not require gadolinium contrast enhancement and can thus provide regional flow information without the confounding permeability factor anticipated in meningioma using dynamic susceptibility contrast techniques.…”

Section: Perspectives and Future Directionsmentioning

confidence: 99%

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Neromyliotis

Κalamatianos

Paschalis

et al. 2020

Magnetic Resonance Imaging

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Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI‐based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. Level of Evidence 5 Technical Efficacy Stage 2

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Characterization of Skull Base Lesions Using Pseudo-Continuous Arterial Spin Labeling

Abstract: The present study demonstrates the high diagnostic performance of pCASL in characterizing skull base lesions by either visual assessment or nTBF quantification. Adding the pCASL sequence to the conventional protocol of skull base assessment can be recommended.

Cited by 16 publications

References 38 publications

Arterial Spin Labeling Applications in Pediatric and Adult Neurologic Disorders

Arterial Spin Labeling Applications in Pediatric and Adult Neurologic Disorders

Imaging of pituitary tumors: an update with the 5th WHO Classifications—part 1. Pituitary neuroendocrine tumor (PitNET)/pituitary adenoma

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

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