Tumour-related imaging parameters predicting the percentage of preserved normal renal parenchyma following nephron sparing surgery: a retrospective study

Eur Radiol. 2013 Jan;23(1):280-6. doi: 10.1007/s00330-012-2582-3. Epub 2012 Jul 16.

Abstract

Objectives: To examine pre-operative imaging parameters that predict the residual amount of healthy renal parenchyma after nephron sparing surgery (NSS) for renal tumours, as this can help stratify patients towards the optimal surgical choice.

Methods: Ninety-eight patients with the diagnosis of a solitary unilateral renal tumour and with pre- and post-operative imaging were included in this retrospective study. Imaging, patient and surgical parameters were acquired and their correlation to the percentage decrease of healthy renal parenchyma following surgery was statistically examined to find the most significant predictor of nephron sparing.

Results: Loss of healthy renal parenchyma was highest in patients with renal sinus tumour involvement (P = 0.003) and anterior tumours (P = 0.006), but not significantly correlated with medial/lateral location (P = 0.940) or exophytic/endophytic tumour growth (P = 0.244). The correlation of tumour size with the percentage of parenchymal sparing did not quite reach statistical significance (P = 0.053), but involvement of the urinary collecting system (P = 0.008) was a very good predictor of complications. Loss of healthy renal parenchyma was higher in patients with high-grade surgical complications (P = 0.001).

Conclusions: Several pre-operative parameters correlate to percentage nephron sparing after NSS. Anterior tumour location and renal sinus involvement proved to be the best predictors of loss of healthy renal parenchyma.

MeSH terms

  • Female
  • Humans
  • Kidney Neoplasms / pathology*
  • Kidney Neoplasms / surgery*
  • Linear Models
  • Male
  • Middle Aged
  • Nephrectomy / methods
  • Nephrons / pathology
  • Nephrons / surgery
  • Postoperative Complications
  • Predictive Value of Tests
  • Preoperative Care
  • Retrospective Studies
  • Statistics, Nonparametric