文章：

淋巴结报告与数据系统（LN-RADS）——浅表淋巴结超声分类的回顾性评估

Lymph Node Reporting and Data System (LN-RADS)—Retrospective Evaluation for Ultrasound Classification of Superficial Lymph Nodes

原文发布日期：18 June 2025

DOI: 10.3390/cancers17122030

类型: Article

开放获取: 是

英文摘要：

Introduction: The evaluation of lymph nodes (LNs) in patients with suspected oncological disease is a crucial factor influencing further diagnostics and management. However, there is a lack of a dedicated system for the precise and comprehensive assessment of LNs. To address this gap, we developed the Lymph Node Reporting and Data System (LN-RADS). Methods: This retrospective multiparametric analysis included the assessment of 719 LNs in 489 patients. The images were evaluated by three radiologists using the LN-RADS scale, assigning each case to one of six group: 1 (normal), 2 (steatotic), 3 (reactive), 4a (low suspicion of malignancy) vs. 4b (high suspicion of malignancy), and 5 (definitely malignant) and were then correlated with histopathological results. The diagnostic performance of LN-RADS was validated. The analysis of 12 morphological features of LNs was performed to identify predictors of malignancy. Results: Histopathological analysis confirmed 389 malignant and 330 benign LNs. LN-RADS achieved 89% sensitivity, 85% specificity, and 87% accuracy in the diagnosis of malignant LN. The observed risk of malignancy by group was 0% for LN-RADS 1, 0% for LN-RADS 2, 2% for LN-RADS 3, 31% for LN-RADS 4a, 77% for LN-RADS 4b, and 97% for LN-RADS 5. Cohen’s kappa statistic indicated substantial inter-reader agreement. Among the evaluated features, the strongest predictor of malignancy was the cortex thickness diameter, with a threshold value of ≥6 mm (82% accuracy; AUC = 0.894). Conclusions: This study demonstrated the high efficacy of the LN-RADS system in distinguishing between benign and malignant lymph nodes and in stratifying malignancy risk. It also showed substantial inter-rater agreement.

摘要翻译： 

引言：对于疑似肿瘤性疾病患者，淋巴结（LNs）的评估是影响后续诊断与治疗决策的关键因素。然而，目前尚缺乏专门用于淋巴结精准全面评估的系统。为填补这一空白，我们开发了淋巴结报告与数据系统（LN-RADS）。方法：这项回顾性多参数分析纳入了489例患者的719枚淋巴结。由三位放射科医师使用LN-RADS量表对影像进行评估，将每个病例归入六个类别之一：1类（正常）、2类（脂肪浸润）、3类（反应性）、4a类（低度可疑恶性）与4b类（高度可疑恶性），以及5类（明确恶性），并与组织病理学结果进行对照分析。研究验证了LN-RADS的诊断效能，并通过分析淋巴结的12项形态学特征来识别恶性病变的预测因子。结果：组织病理学分析确认389枚为恶性淋巴结，330枚为良性淋巴结。LN-RADS系统诊断恶性淋巴结的敏感性为89%，特异性为85%，准确率为87%。按类别观察到的恶性风险分别为：LN-RADS 1类0%、2类0%、3类2%、4a类31%、4b类77%、5类97%。Cohen's kappa统计量显示观察者间一致性较高。在评估的特征中，皮质厚度直径是最强的恶性预测因子，其临界值为≥6毫米（准确率82%；AUC = 0.894）。结论：本研究证明LN-RADS系统在区分良恶性淋巴结及分层恶性风险方面具有高效能，同时显示出良好的观察者间一致性。

原文链接：

Lymph Node Reporting and Data System (LN-RADS)—Retrospective Evaluation for Ultrasound Classification of Superficial Lymph Nodes