多模态影像在退变性椎间盘疾病诊断中的进展与应用。

IF 2.3 Frontiers in radiology Pub Date : 2025-03-06 eCollection Date: 2025-01-01 DOI:10.3389/fradi.2025.1298054
Eric M Teichner, Robert C Subtirelu, Connor R Crutchfield, Chitra Parikh, Arjun Ashok, Sahithi Talasila, Victoria Anderson, Milan Patel, Sricharvi Mannam, Andrew Lee, Thomas Werner, William Y Raynor, Abass Alavi, Mona-Elisabeth Revheim
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引用次数: 0

摘要

椎间盘退行性疾病(DDD)是一种常见的脊柱疾病,其特征是椎间盘恶化,导致慢性背痛和活动能力降低。虽然磁共振成像(MRI)一直是晚期DDD诊断的标准,但其在早期检测方面的局限性促使人们探索先进的成像方法。使用18F-氟脱氧葡萄糖(FDG)和18F-氟化钠(NaF)的正电子发射断层扫描/计算机断层扫描(PET/CT)在识别代谢失衡和年龄相关的脊柱退变方面显示出希望,从而补充了疾病的CT分级。新型的PET/MRI混合成像模式提供了新的机会,并简要讨论。DDD的复杂病理生理解剖强调遗传易感性和生活方式因素,如吸烟和肥胖的作用。这些病因因素显著影响腰骶区,表现为慢性腰痛(LBP)和潜在的神经压迫。传统的分级系统,如MRI的Pfirrmann分级,因其在捕获DDD全谱方面的局限性而受到评估。还简要提到了利用人工智能(AI)识别早期疾病过程和预测患者预后的潜力。总体而言,该手稿旨在聚焦DDD成像技术的进步,强调其在完善诊断和治疗策略方面的意义。正在进行的和未来的研究的作用是强调验证这些新兴技术和克服目前的限制,更有效的早期检测和治疗。
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The advancement and utility of multimodal imaging in the diagnosis of degenerative disc disease.

Degenerative disc disease (DDD) is a common spinal condition characterized by the deterioration of intervertebral discs, leading to chronic back pain and reduced mobility. While magnetic resonance imaging (MRI) has long been the standard for late-stage DDD diagnosis, its limitations in early-stage detection prompt the exploration of advanced imaging methods. Positron emission tomography/computed tomography (PET/CT) using 18F- fluorodeoxyglucose (FDG) and 18F-sodium fluoride (NaF) has shown promise in identifying metabolic imbalances and age-related spinal degeneration, thereby complementing CT grading of the disease. The novel hybrid imaging modality PET/MRI provides new opportunities and are briefly discussed. The complex pathophysiology of DDD is dissected to highlight the role of genetic predisposition and lifestyle factors such as smoking and obesity. These etiological factors significantly impact the lumbosacral region, manifesting in chronic low back pain (LBP) and potential nerve compression. Traditional grading systems, like the Pfirrmann classification for MRI, are evaluated for their limitations in capturing the full spectrum of DDD. The potential to identify early disease processes and predict patient outcomes by the use of artificial intelligence (AI) is also briefly mentioned. Overall, the manuscript aims to spotlight advancements in imaging technologies for DDD, emphasizing their implications in refining both diagnosis and treatment strategies. The role of ongoing and future research is emphasized to validate these emerging techniques and overcome current limitations for more effective early detection and treatment.

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