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基于MRI的神经病评分报告和数据系统 (NS-RADS):在32名放射学读者中进行了对外围神经病状的多机构更广泛经验可用性研究

Avneesh Chhabra^1,2,3,4,5, Flavio Duarte Silva⁶, Bayan Mogharrabi^6,7

¹Department of Radiology, UT Southwestern Medical Center, Dallas, TX, 75390-9178, USA. avneesh.chhabra@utsouthwestern.edu.

European radiology

|January 20, 2024

概括

神经病评分报告和数据系统 (NS-RADS) 允许非专家放射科医生准确诊断外围神经病 (PN) 具有良好的可靠性. 这种基于MRI的系统对经验丰富和一般放射科医生都有效.

科学领域:

放射学放射学是一门学科.
神经学神经学
医疗成像医学成像

背景情况:

诊断外围神经病变 (PN) 可能具有挑战性.
标准化评分系统可以提高诊断准确性和可靠性.

研究的目的:

评估神经病评分报告和数据系统 (NS-RADS) 的读者间可靠性和诊断性能.
在经过有限的培训后,在具有不同经验水平的放射科医生中评估NS-RADS的有效性.

主要方法:

这是一项多机构的回顾性研究,涉及32名具有不同经验水平的放射学读者.
读者接受了关于NS-RADS分类系统的结构化培训.
使用Conger的卡帕评估了读者之间的一致性,并计算了诊断准确性.

主要成果:

在NS-RADS类别中观察到良好的读者间协议,在子类别中观察到适度的协议.
实习生的表现与主持人的表现相当,神经的准确率为71%vs75%,肌肉的准确率为86.6%vs87.5%.
在这两组中,NS-RADS的准确度都超过了平均可信的诊断准确度.

结论:

该NS-RADS评分系统显示出良好的准确性和可靠性,用于诊断外围神经病变的非专家放射科医生.
NS-RADS是一种基于MRI的实用工具,用于外围神经肌肉疾病的诊断和严重程度分级.
该系统适用于经验丰富和普通放射科医生.

关键词:

陷入神经病变 - 陷入神经病变磁共振成像技术磁共振成像技术周围神经受伤的情况.周围神经膜瘤是周围神经膜瘤.周围神经病变是一种神经病变.

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