Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9178.
Department of Orthopedic Surgery, UT Southwestern Medical Center, Dallas, TX.
AJR Am J Roentgenol. 2022 Aug;219(2):279-291. doi: 10.2214/AJR.22.27422. Epub 2022 Mar 2.
A standardized guideline and scoring system would improve evaluation and reporting of peripheral neuropathy (PN) on MRI. The objective of this study was to create and validate a neuropathy classification and grading system, which we named the Neuropathy Score Reporting and Data System (NS-RADS). This retrospective study included 100 patients with nerve imaging studies and known clinical diagnoses. Experts crafted NS-RADS using mutually agreed-on qualitative criteria for the classification and grading of PN. Different classes were created to account for the spectrum of underlying pathologies: unremarkable (U), injury (I), neoplasia (N), entrapment (E), diffuse neuropathy (D), not otherwise specified (NOS), and postintervention state (PI). Subclasses were established to describe the severity or extent of the lesions. Validation testing was performed by 11 readers from 10 institutions with experience levels ranging from 3 to 18 years after residency. After initial reader training, cases were presented to readers who were blinded to the final clinical diagnoses. Interobserver agreement was assessed using correlation coefficients and the Conger kappa, and accuracy testing was performed. Final clinical diagnoses included normal ( = 5), nerve injury ( = 25), entrapment ( = 15), neoplasia ( = 33), diffuse neuropathy ( = 18), and persistent neuropathy after intervention ( = 4). The miscategorization rate for NS-RADS classes was 1.8%. Final diagnoses were correctly identified by readers in 71-88% of cases. Excellent inter-reader agreement was found on the NS-RADS pathology categorization (κ = 0.96; 95% CI, 0.93-0.98) as well as muscle pathology categorization (κ = 0.76; 95% CI, 0.68-0.82). The accuracy for determining milder versus more severe categories per radiologist ranged from 88% to 97% for nerve lesions and from 86% to 94% for muscle abnormalities. The proposed NS-RADS classification is accurate and reliable across different reader experience levels and a spectrum of PN conditions. NS-RADS can be used as a standardized guideline for reporting PN and improved multidisciplinary communications.
规范化指南和评分系统将改善磁共振成像(MRI)外周神经病变(PN)的评估和报告。本研究的目的是创建和验证一种神经病分类和分级系统,我们将其命名为神经病变评分报告和数据系统(NS-RADS)。这项回顾性研究纳入了 100 例有神经影像学检查和已知临床诊断的患者。专家使用神经病变分类和分级的共同商定的定性标准制定了 NS-RADS。创建不同的类别是为了说明潜在病理学的范围:正常(U)、损伤(I)、肿瘤(N)、受压(E)、弥漫性神经病(D)、无法分类(NOS)和干预后状态(PI)。亚类用于描述病变的严重程度或范围。10 家机构的 11 名有 3 至 18 年住院后经验的读者进行了验证性测试。在初始读者培训后,向对最终临床诊断不知情的读者展示病例。使用相关系数和康格尔 kappa 评估观察者间一致性,并进行准确性测试。最终临床诊断包括正常(n = 5)、神经损伤(n = 25)、受压(n = 15)、肿瘤(n = 33)、弥漫性神经病(n = 18)和干预后持续神经病(n = 4)。NS-RADS 类别的错误分类率为 1.8%。在 71%-88%的病例中,读者正确识别了最终诊断。在 NS-RADS 病理学分类(κ = 0.96;95%CI,0.93-0.98)和肌肉病理学分类(κ = 0.76;95%CI,0.68-0.82)方面,读者之间的一致性非常好。每位放射科医生确定轻度与更严重类别的准确性为神经病变的 88%-97%,肌肉异常的 86%-94%。所提出的 NS-RADS 分类在不同的读者经验水平和一系列 PN 情况下是准确可靠的。NS-RADS 可作为报告 PN 和改善多学科沟通的标准化指南。