Department of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Department of Surgery "Pietro Valdoni", Policlinico "Umberto I", Rome "Sapienza" University of Rome, 00128, Rome, Italy.
Eur Radiol. 2024 Dec;34(12):7734-7745. doi: 10.1007/s00330-024-10828-y. Epub 2024 Jun 12.
The Node-RADS score was recently introduced to offer a standardized assessment of lymph node invasion (LNI). We tested its diagnostic performance in accurately predicting LNI in breast cancer (BC) patients with magnetic resonance imaging. The study also explores the consistency of the score across three readers.
A retrospective study was conducted on BC patients who underwent preoperative breast contrast-enhanced magnetic resonance imaging and lymph node dissection between January 2020 and January 2023. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated for different Node-RADS cut-off values. Pathologic results were considered the gold standard. The overall diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). A logistic regression analysis was performed. Cohen's Kappa analysis was used for inter-reader agreement.
The final population includes 192 patients and a total of 1134 lymph nodes analyzed (372 metastatic and 762 benign). Increasing the Node-RADS cut-off values, specificity and PPV rose from 71.4% to 100% and 76.7% to 100%, respectively, for Reader 1, 69.4% to 100% and 74.6% to 100% for Reader 2, and from 64.3% to 100% and 72% to 100% for Reader 3. Node-RADS > 2 could be considered the best cut-off value due to its balanced performance. Node-RADS exhibited a similar AUC for the three readers (0.97, 0.93, and 0.93). An excellent inter-reader agreement was found (Kappa values between 0.71 and 0.83).
The Node-RADS score demonstrated moderate-to-high overall accuracy in identifying LNI in patients with BC, suggesting that the scoring system can aid in the identification of suspicious lymph nodes and facilitate appropriate treatment decisions.
Node-RADS > 2 can be considered the best cut-off for discriminating malignant nodes, suggesting that the scoring system can effectively help identify suspicious lymph nodes by staging the disease and providing a global standardized language for clear communication.
Axillary lymphadenopathies in breast cancer are crucial for determining the disease stage. Node-RADS was introduced to provide a standardized evaluation of breast cancer lymph nodes. RADS > 2 can be considered the best cut-off for discriminating malignant nodes.
最近引入了 Node-RADS 评分，以提供对淋巴结侵犯（LNI）的标准化评估。我们测试了其在使用磁共振成像准确预测乳腺癌（BC）患者 LNI 方面的诊断性能。该研究还探讨了评分在三位读者中的一致性。
对 2020 年 1 月至 2023 年 1 月期间接受术前乳腺对比增强磁共振成像和淋巴结清扫术的 BC 患者进行了回顾性研究。为不同的 Node-RADS 截断值计算了敏感性、特异性、阳性预测值（PPV）和阴性预测值。病理结果被认为是金标准。使用受试者工作特征曲线和曲线下面积（AUC）评估整体诊断性能。进行了逻辑回归分析。使用 Cohen's Kappa 分析评估读者间的一致性。
最终人群包括 192 名患者，共分析了 1134 个淋巴结（372 个转移性和 762 个良性）。随着 Node-RADS 截断值的增加，Reader 1 的特异性和 PPV 从 71.4%上升到 100%和 76.7%上升到 100%，Reader 2 从 69.4%上升到 100%和 74.6%上升到 100%，Reader 3 从 64.3%上升到 100%和 72%上升到 100%。由于其平衡性能，Node-RADS > 2 可被视为最佳截断值。Node-RADS 对三位读者的 AUC 表现相似（0.97、0.93 和 0.93）。发现读者间具有极好的一致性（Kappa 值在 0.71 到 0.83 之间）。
Node-RADS 评分在识别 BC 患者的 LNI 方面表现出中等至高的总体准确性，表明评分系统可以帮助识别可疑淋巴结并促进适当的治疗决策。
Node-RADS > 2 可被视为区分恶性淋巴结的最佳截断值，这表明该评分系统可以通过分期疾病来有效帮助识别可疑淋巴结，并提供一种用于清晰沟通的全球标准化语言。
乳腺癌腋窝淋巴结对确定疾病分期至关重要。引入了 Node-RADS 以提供对乳腺癌淋巴结的标准化评估。RADS > 2 可被视为区分恶性淋巴结的最佳截断值。
