Romano Andrea, Romano Allegra, Moltoni Giulia, de Rosa Giulia, D'Eufemia Silvia, Blandino Antonella, Monopoli Cristiana, Zema Lidia, De Giorgi Sara, Tristano Sara, Ius Tamara, Rega Alessia, Mancini Vittorio Pio, Madonia Gabriele, Bozzao Alessandro
Department of Neurosciences, Mental Health and Sensory Organs, School of Medicine and Psychology, "Sapienza" University, S.Andrea Hospital, Via Di Grottarossa 1035, 00136, Rome, Italy.
Diagnostic Imaging Unit, University Hospital of Rome Tor Vergata, Rome, Italy.
Radiol Med. 2025 Jul 15. doi: 10.1007/s11547-025-02048-1.
This review examines the distinctive role of arterial spin labelling (ASL) in neuro-oncology. ASL is a completely non-invasive MRI technique that quantifies cerebral perfusion without exogenous contrast agents, making it an attractive alternative to dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) perfusion-particularly WHEN gadolinium cannot be administered or when serial follow-up studies are required. Unlike DSC, ASL is immune to magnetic susceptibility artefacts, and, unlike DCE, it does not demand lengthy acquisitions or complex post-processing. The available evidence indicates that ASL performs well in grading gliomas and in characterizing brain metastases, lymphomas, and meningiomas. Its superiority over other perfusion methods becomes most apparent in longitudinal follow-up of cerebral gliomas, where it reliably tracks haemodynamic changes, and in assessing tumour-related conditions such as epilepsy and paraneoplastic syndromes. Overall, ASL offers a repeatable and dependable assessment of tumour perfusion and vascularity, thereby supporting more accurate diagnosis, grading, and treatment monitoring in neuro-oncology.
本综述探讨了动脉自旋标记(ASL)在神经肿瘤学中的独特作用。ASL是一种完全无创的MRI技术,无需外源性造影剂即可量化脑灌注,这使其成为动态磁敏感对比(DSC)和动态对比增强(DCE)灌注的有吸引力的替代方法,尤其是在无法使用钆剂或需要进行系列随访研究时。与DSC不同,ASL不受磁敏感伪影的影响,与DCE不同,它不需要长时间采集或复杂的后处理。现有证据表明,ASL在胶质瘤分级以及脑转移瘤、淋巴瘤和脑膜瘤的特征性诊断方面表现良好。其相对于其他灌注方法的优势在脑胶质瘤的纵向随访中最为明显,在该过程中它能可靠地追踪血流动力学变化,并且在评估癫痫和副肿瘤综合征等肿瘤相关病症时也具有优势。总体而言,ASL为肿瘤灌注和血管生成提供了可重复且可靠的评估,从而有助于神经肿瘤学中更准确的诊断、分级和治疗监测。