Leonardo Costantino, Flammia Rocco Simone, Lucciola Sara, Proietti Flavia, Pecoraro Martina, Bucca Bruno, Licari Leslie Claire, Borrelli Antonella, Bologna Eugenio, Landini Nicholas, Del Monte Maurizio, Chung Benjamin I, Catalano Carlo, Magliocca Fabio Massimo, De Berardinis Ettore, Del Giudice Francesco, Panebianco Valeria
Department of Maternal Infant and Urological Sciences, Sapienza University Rome, Policlinico Umberto I Hospital, 00161 Rome, Italy.
Department of Radiological Sciences, Oncology and Pathology, Sapienza University, 00161 Rome, Italy.
Cancers (Basel). 2023 Jan 18;15(3):580. doi: 10.3390/cancers15030580.
BACKGROUND: Current cross-sectional imaging modalities exhibit heterogenous diagnostic performances for the detection of a lymph node invasion (LNI) in bladder cancer (BCa) patients. Recently, the Node-RADS score was introduced to provide a standardized comprehensive evaluation of LNI, based on a five-item Likert scale accounting for both size and configuration criteria. In the current study, we hypothesized that the Node-RADS score accurately predicts the LNI and tested its diagnostic performance. METHODS: We retrospectively reviewed BCa patients treated with radical cystectomy (RC) and bilateral extended pelvic lymph node dissection, from January 2019 to June 2022. Patients receiving preoperative systemic chemotherapy were excluded. A logistic regression analysis tested the correlation between the Node-RADS score and LNI both at patient and lymph-node level. The ROC curves and the AUC depicted the overall diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for different cut-off values (>1, >2, >3, >4). RESULTS: Overall, data from 49 patients were collected. Node-RADS assigned on CT scans images, was found to independently predict the LNI after an adjusted multivariable regression analysis, both at the patient (OR 3.36, 95%CI 1.68-9.40, = 0.004) and lymph node (OR 5.18, 95%CI 3.39-8.64, < 0.001) levels. Node-RADS exhibited an AUC of 0.87 and 0.91 at the patient and lymph node levels, respectively. With increasing Node-RADS cut-off values, the specificity and PPV increased from 57.1 to 97.1% and from 48.3 to 83.3%, respectively. Conversely, the sensitivity and NPV decreased from 100 to 35.7% and from 100 to 79.1%, respectively. Similar trends were recorded at the lymph node level. Potentially, Node-RADS > 2 could be considered as the best cut-off value due to balanced values at both the patient (77.1 and 78.6%, respectively) and lymph node levels (82.4 and 93.4%, respectively). CONCLUSIONS: The current study lays the foundation for the introduction of Node-RADS for the regional lymph-node evaluation in BCa patients. Interestingly, the Node-RADS score exhibited a moderate-to-high overall accuracy for the identification of LNI, with the possibility of setting different cut-off values according to specific clinical scenarios. However, these results need to be validated on larger cohorts before drawing definitive conclusions.
背景：目前的横断面成像模式在检测膀胱癌（BCa）患者的淋巴结转移（LNI）方面表现出异质性的诊断性能。最近，引入了淋巴结影像报告和数据系统（Node-RADS）评分，以基于考虑大小和形态标准的五项李克特量表对LNI进行标准化综合评估。在本研究中，我们假设Node-RADS评分能够准确预测LNI，并测试其诊断性能。 方法：我们回顾性分析了2019年1月至2022年6月接受根治性膀胱切除术（RC）和双侧扩大盆腔淋巴结清扫术的BCa患者。排除接受术前全身化疗的患者。逻辑回归分析在患者和淋巴结水平测试Node-RADS评分与LNI之间的相关性。ROC曲线和AUC描述了总体诊断性能。此外，还计算了不同临界值（>1、>2、>3、>4）下的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。 结果：总体而言，收集了49例患者的数据。经调整的多变量回归分析发现，CT扫描图像上的Node-RADS评分在患者（OR 3.36，95%CI 1.68-9.40，P = 0.004）和淋巴结（OR 5.18，95%CI 3.39-8.64，P < 0.001）水平上均能独立预测LNI。Node-RADS在患者和淋巴结水平的AUC分别为0.87和0.91。随着Node-RADS临界值的增加，特异性和PPV分别从57.1%增加到97.1%，从48.3%增加到83.3%。相反，敏感性和NPV分别从100%下降到35.7%，从100%下降到79.1%。在淋巴结水平也记录到了类似趋势。由于在患者（分别为77.1%和78.6%）和淋巴结水平（分别为82.4%和93.4%）的数值平衡，潜在地，Node-RADS > 2可被视为最佳临界值。 结论：本研究为在BCa患者中引入Node-RADS进行区域淋巴结评估奠定了基础。有趣的是，Node-RADS评分在识别LNI方面表现出中到高的总体准确性，有可能根据特定临床情况设置不同的临界值。然而，在得出明确结论之前，这些结果需要在更大的队列中进行验证。
Abdom Radiol (NY). 2025-1
Eur Urol Oncol. 2022-8
Zotero 插件
