Discover Oncology (Jul 2023)
Perirenal fat stranding as a predictor of disease progression after radical nephroureterectomy for renal pelvic urothelial carcinoma: a retrospective study
Abstract
Abstract Background To investigate the impact of Perirenal fat stranding (PRFS) on progression after radical nephroureterectomy (RNU) for renal pelvic urothelial carcinoma (RPUC) without hydronephrosis and to reveal the pathological findings of PRFS. Methods Clinicopathological data, including computed tomography (CT) findings of the ipsilateral PRFS, were collected from the medical records of 56 patients treated with RNU for RPUC without hydronephrosis between 2011 and 2021 at our institution. PRFS on CT was classified as either low or high PRFS. The impact of PRFS on progression-free survival (PFS) after RNU was analyzed using the Kaplan–Meier method and log-rank test. In addition, specimens including sufficient perirenal fat from patients with low and with high PRFS were pathologically analyzed. Immunohistochemical analysis of CD68, CD163, CD3, and CD20 was also performed. Results Of the 56 patients, 31(55.4%) and 25 (44.6%) patients were classified as having low and high PRFS, respectively. Within a median follow-up of 40.6 months postoperatively, 11 (19.6%) patients showed disease progression. The Kaplan–Meier method and log-rank test revealed that patients with high PRFS had significantly lower PFS rates than those with low PRFS (3-year PFS 69.8% vs 93.3%; p = 0.0393). Pathological analysis revealed that high PRFS specimens (n = 3 patients) contained more fibrous strictures in perirenal fat than low PRFS specimens (n = 3 patients). In addition, M2 macrophages (CD163 +) infiltrating fibrous tissue in perirenal area were observed in all patients with high PRFS group. Conclusions PRFS of RPUC without hydronephrosis consists of collagenous fibers with M2 macrophages. The presence of ipsilateral high PRFS might be a preoperative risk factor for progression after RNU for RPUC patients without hydronephrosis. Prospective studies with large cohorts are required in the future.
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