Purpose: Computed tomography (CT) is the most useful diagnostic modality for staging renal cell carcinoma (RCC). However, CT is limited in its ability to predict renal sinus fat invasion (SFI). Here, we aimed to evaluate whether preoperative neutrophil-to-lymphocyte ratio (NLR) could predict pathological SFI in patients with RCC of ≤7 cm for whom preoperative imaging reveals potential renal SFI. Materials and Methods: We reviewed the medical records of 1311 patients who underwent extirpative renal surgery for non-metastatic RCC of ≤7 cm between November 2005 and December 2014. After excluding patients with no SFI in preoperative imaging, unavailable preoperative data, and morbidity affecting inflammatory markers, a total of 476 patients were included in this study. Multivariate logistic regression analysis was used to evaluate predictors of pathological SFI. Results: We implemented a cut-off value of 1.98, which was calculated by ROC analysis to obtain high (≥1.98) and low (<1.98) NLR groups. A total of 93 patients with pathological SFI had larger clinical tumor size, higher preoperative NLR, larger pathological tumor size, more frequent renal vein involvement, and higher Fuhrman nuclear grade. Multivariate analysis indicated that high NLR [odds ratio (OR) 2.032, p=0.004], clinical tumor size (OR 1.586, p<0.001), and collecting system involvement on preoperative imaging (OR 3.957, p=0.011) were significantly associated with pathological SFI in these tumors. Conclusion: Preoperative high NLR was associated with pathological SFI in patients with RCC of ≤7 cm and presumed SFI on preoperative imaging. Greater surgical attention is needed to obtain negative margins during partial nephrectomy in these patients.
Bibliographical noteFunding Information:
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1095). We are especially grateful to Young Taik Oh for CT data interpretation and Nam Hoon Cho for histopathological reading of the specimens.
© Yonsei University College of Medicine 2019.
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