TY - JOUR
T1 - Preoperative Computed Tomography Assessment for Perinephric Fat Invasion
T2 - Comparison with Pathological Staging
AU - Landman, Jaime
AU - Park, Jae Young
AU - Zhao, Chenhui
AU - Baker, Molly
AU - Hofmann, Martin
AU - Helmy, Mohammad
AU - Lall, Chandana
AU - Bozoghlanian, Mari
AU - Okhunov, Zhamshid
N1 - Publisher Copyright:
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
KW - carcinoma
KW - kidney neoplasms
KW - multi-detector-row computed tomography
KW - neoplasm staging
KW - renal cell
UR - http://www.scopus.com/inward/record.url?scp=85015186360&partnerID=8YFLogxK
U2 - 10.1097/RCT.0000000000000588
DO - 10.1097/RCT.0000000000000588
M3 - Article
C2 - 28296683
AN - SCOPUS:85015186360
SN - 0363-8715
VL - 41
SP - 702
EP - 707
JO - Journal of Computer Assisted Tomography
JF - Journal of Computer Assisted Tomography
IS - 5
ER -