2898
Diagnostic and Prognostic Value of MRI-based Node-RADS for Regional Lymph Node Metastasis in Renal Cell Carcinoma1Department of Radiology, Chinese PLA General Hospital, Beijing, China, 2Chinese PLA General Hospital, Beijing, China
Synopsis
Keywords: Kidney, Tumor, Carcinoma, Renal Cell; Lymphatic Metastasis; Nephrectomy; Magnetic resonance imaging; Prognosis.
Motivation: The prognostic property of regional lymph node metastasis (RLNM) has been widely recognized, but the diagnostic workup has stagnated for renal cell carcinoma (RCC).
Goal(s): This study aimed to assess the diagnostic performance of MRI-based Node Reporting and Data System (Node-RADS) for RLNM and to explore its prognostic impact on patients with RCC.
Approach: A single-center retrospective comparative study.
Results: MRI-based Node-RADS presented better diagnostic performance for RLNM than the size criteria and previous clinical models (AUC, 0.91 vs 0.79–0.85; all P<.05), and exhibited a substantial prognostic value for RCCs regarding progress-free survival and overall survival (both P<.001).
Impact: Node-RADS, a concept that combines size, texture, margin, and shape, is a promising approach for lymph node metastasis in RCCs, which may contribute to improving clinical node staging and guiding clinical decision making.
Introduction
Renal cell carcinoma (RCC) is the predominant type of kidney cancer, accounting for about 90% of all cases [1]. Nephrectomy is the main treatment option; however, whether to perform regional lymph node dissection during surgery remains debatable due to the limited accuracy in detecting regional lymph node metastasis (RLNM). [2–4]. Several preoperative assessment methods are being explored for RLNM in patients with RCC, but their diagnostic efficacy is suboptimal [5–8]. Node Reporting and Data System (Node-RADS), a scoring system for standardized assessment of node involvement in cancer, may be able to unlock the deadlock [9]. In this study, we aimed to assess the efficacy of MRI-based Node-RADS in diagnosing RLNM and to estimate its prognostic significance in RCCs.Methods
This retrospective study included patients with RCC who underwent nephrectomy and regional lymph node dissection between January 2010 and October 2022. Using MRI-based Node-RADS with (eNode-RADS) and without (uNode-RADS) reference to enhanced images, two senior radiologists scored lymph nodes in consensus. The performance of RLNM detection was compared with the size criteria and previous models constructed by Li, Kara, and Umberto. Additionally, two radiologists and one urologist scored all lesions to assess the interobserver agreement. Progress-free survival (PFS) and overall survival (OS) were estimated and compared between patients with low (1–3) and high (4–5) scores.Results
Overall, 201 patients with RCC (147 men; median age, 54 [46, 62] years) were enrolled, including 57 with RLNM. In diagnosing RLNM, eNode-RADS showed better performance than uNode-RADS with higher specificity (96.53% vs. 92.36%, P=0.03) and superior interobserver agreement (weighted κ = 0.74 vs. 0.67; P=0.003). Furthermore, eNode-RADS outperformed the size criteria and previous clinical models (area under the curves, 0.91 vs. 0.79–0.85; all P<0.001). During a 57-month median follow-up, high-scoring patients experienced poorer PFS (median, 17 months vs. 116 months, P<0.001) and OS (median, 29 months vs. not reached, P<0.001) than low-scoring patients. According to multivariable Cox models adjusted for distant metastasis, pathological T stage, RCC subtype, systemic therapy, and surgical methods, eNode-RADS remained an independent predictor of PFS (hazard ratio [HR], 1.77; 95% confidence interval [CI], 1.06–2.96; P=0.03) and OS (HR, 2.80; 95% CI, 1.56–5.02; P<0.001).Discussion
To date, few studies involved the clinical node staging of RCCs based on various imaging modalities [10, 11], and only three of them had a reasonable sample size (>10 with positive nodes) and complete pathological evidence [12–14]. They showed a sensitivity range of 63%–77% and a specificity range of 75%–82% in detecting RLNM using criteria such as a short diameter of ≥ 10 mm or an apparent diffusion coefficient of <1.25×10-3 mm2/s. In this context, Node-RADS, a concept that combines size, texture, margin, and shape, may be a promising approach for node evaluation in RCCs. Our findings validated the hypothesis that Node-RADS had an impressive performance compared with the short diameter and apparent diffusion coefficient.In the MRI algorithm of Node-RADS, unlike that of computed tomography (CT), contrast agent use is not mandatory, given the superior soft-tissue contrast. However, unenhanced MRI may be insufficient to assess the subtle architecture of RCC-draining lymph nodes due to the retroperitoneal anatomical complexity, small RLN size, and perifocal edema [15], particularly when accompanied by vein thrombosis or sinus invasion. In this study, compared with uNode-RADS, eNode-RADS yielded a favourable interobserver agreement and a superior diagnostic efficacy of RLNs with higher specificity (96.53% vs. 92.36%), reflecting the accurate exclusion of benign enlarged RLNs. Nevertheless, both eNode-RADS (77.19%) and uNode-RADS (70.18%) yielded suboptimal sensitivity, which was attributed to the inability to identify micro-metastases. Incorporating other features, such as SI on T2WI, SI on DWI, and rim enhancement of RLNs, may optimize Node-RADS and improve its sensitivity.
Unlike other RADS algorithms used for lesion-specific risk assessment, Node-RADS presets a clinical scenario of clinical node staging, that is, the accurate detection of positive lymph nodes in the tumour-draining area. As a primary goal of tumour staging, we evaluated the long-term prognosis of patients with RCC, and eNode-RADS demonstrated excellent stratification capabilities for PFS and OS. Especially after adjusting for recognized prognostic factors, Node-RADS still had independent prognostic significance. As expected, the OS distribution based on different eNode-RADS scores in this study roughly corresponded to that based on different RLNM statuses in previous literature [16,17], confirming the consistency between Node-RADS and RLNM.
Conclusion
MRI-based Node-RADS, especially contrast-enhanced MRI-based Node-RADS, demonstrated outstanding performance in detecting RLNM and distinct prognostic value for RCCs, which may contribute to improving clinical node staging and guiding clinical decision making.Acknowledgements
We acknowledge the patients whose samples/data provided the foundation for this study, and are grateful to the multidisciplinary team of urology in the Chinese PLA General Hospital for their support and assistance. This work was supported by the National Natural Science Foundation of China (Grant 81971580 and 82271951), Beijing Natural Science Foundation (Grant 7222167) and the Youth Independent Innovation Science Foundation of Chinese PLA General Hospital (Grant 22QNFC061).References
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