背景:提出了节点报告和数据系统(Node-RADS),并且可以应用于所有解剖部位的淋巴结(LN)。本研究旨在探讨Node-RADS在宫颈癌患者中的诊断表现。
方法:回顾性纳入81例接受根治性子宫切除术和LN切除术的宫颈癌患者。由两名放射科医师对所有患者进行术前MRI扫描,在LN水平和患者水平。采用卡方和Fisher精确检验来评估不同地区有和没有LN转移(LNM)的患者在大小和构型上的分布差异。使用受试者工作特征(ROC)和曲线下面积(AUC)来探索Node-RADS评分对LNM的诊断性能。
结果:主动脉旁的LNM率,髂总,髂内,外髂关节,腹股沟区占7.4%,9.3%,19.8%,21.0%,和2.5%,分别。在患者层面,随着NODE-RADS评分的增加,LNM的比率也增加了,率为26.1%,29.2%,42.9%,80.0%,Node-RADS评分1、2、3、4和5分别为90.9%。在患者层面,Node-RADS评分>1,>2,>3和>4的AUC分别为0.632,0.752,0.763和0.726.在患者水平和LN水平,节点-RADS评分>3可以被认为是具有最佳AUC和准确性的最佳截止值。
结论:Node-RADS可有效预测4至5分的LNM。然而,在患者级别,评分1和2的LNM比例超过25%,这与这些评分预期的极低和低概率LNM不一致.
BACKGROUND: Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This
study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients.
METHODS: A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher\'s exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM.
RESULTS: The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy.
CONCLUSIONS: Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.