Preoperative Computed Tomography Assessment for Perinephric Fat Invasion: Comparison with Pathological Staging

Jaime Landman, Jae Young Park, Chenhui Zhao, Molly Baker, Martin Hofmann, Mohammad Helmy, Chandana Lall, Mari Bozoghlanian, Zhamshid Okhunov

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Objective The aim of this study was to assess the accuracy of computed tomography (CT) imaging in diagnosing perinephric fat (PNF) invasion in patients with renal cell carcinoma. Methods We retrospectively reviewed the medical records and preoperative CT images of 161 patients (105 men and 56 women) for pT1-pT3a renal cell carcinoma. We analyzed the predictive accuracy of CT criteria for PNF invasion stratified by tumor size. We determined the predictive value of CT findings in diagnosing PNF invasion using logistic regression analysis. Results The overall accuracy of perinephric (PN) soft-tissue stranding, peritumoral vascularity, increased density of the PNF, tumoral margin, and contrast-enhancing soft-tissue nodule to predict PNF invasion were 56%, 59%, 35%, 80%, and 87%, respectively. Perinephric soft-tissue stranding and peritumoral vascularity showed high sensitivity but low specificity regardless of tumor size. A contrast-enhancing soft-tissue nodule showed low sensitivity but high specificity in predicting PNF invasion. Among tumors 4 cm or less, PN soft-tissue stranding showed 100% sensitivity and 70% specificity, and tumor margin showed 100% sensitivity and 98% specificity. Among CT criteria for PNF invasion, PN soft-tissue stranding was chosen as the only significant factor for assessing PNF invasion by logistic regression analysis. Conclusions Computed tomography does not seem to reliably predict PNF invasion. However, PN soft-tissue stranding was shown to be the only significant factor for predicting PNF invasion, which showed good accuracy with high sensitivity and high specificity in tumors 4 cm or less.

Original languageEnglish
Pages (from-to)702-707
Number of pages6
JournalJournal of Computer Assisted Tomography
Volume41
Issue number5
DOIs
Publication statusPublished - 2017 Sep 1

Keywords

  • carcinoma
  • kidney neoplasms
  • multi-detector-row computed tomography
  • neoplasm staging
  • renal cell

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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