Node-RADS category on preoperative CT predicts prognosis in patients with papillary renal cell carcinoma.

Abstract

This research focused on investigating the relationship between the Node Reporting and Data System (Node-RADS) categories, determined via preoperative CT, and the outcomes of progression-free survival (PFS) and cancer-specific survival (CSS) in individuals diagnosed with papillary renal cell carcinoma (pRCC). A retrospective multicenter study initially enrolled 454 patients, with 218 eligible for analysis following partial nephrectomy or radical resection for pRCC. Prognostic factors related to PFS and CSS in pRCC patients were identified through univariate and multivariate Cox regression analyses. Subsequently, the prognostic value of Node-RADS was assessed and compared with the existing pRCC risk stratification model. In total, 218 patients (mean age, 58 years; men, 164 [75.2%]) with pRCC (186 Node-rads I tumors (85.3%), 10 Node-rads II tumors (4.6%), and 22 Node-rads III tumors (10.1%)) were included. The Node-RADS category emerged as an independent prognostic factor for PFS (III vs II vs I, hazard ratio (HR) 4.250, p < 0.001) and CSS (III vs II vs I, HR 4.466; p < 0.001). When the Node-RADS category was incorporated into Leibovich's model, the resulting combined model demonstrated a significant improvement in predictive accuracy (C-index: 0.865 versus 0.755, p = 0.005 for PFS; and 0.921 versus 0.835, p = 0.01 for CSS). The Node-RADS category has been identified as a more accurate predictor of prognosis for pRCC, regardless of pathologic lymph node involvement. These findings need further confirmation in prospective studies. Question Lymph node status is important for papillary renal cell carcinoma prognosis, and there is a lack of consensus on radiological evaluation. Findings Node-RADS is an independent predictor of progression-free survival and cancer-specific survival in papillary renal cell carcinoma. Clinical relevance The Node Reporting and Data System category improves the accuracy of the Leibovich model for prognosis, which can help clinical practitioners select individualized treatment plans for each patient.