Literature DB >> 23706868

The value of "liver windows" settings in the detection of small renal cell carcinomas on unenhanced computed tomography.

Kamal Sahi1, Stuart Jackson1, Edward Wiebe1, Gavin Armstrong1, Sean Winters1, Ronald Moore2, Gavin Low3.   

Abstract

OBJECTIVE: To assess if "liver window" settings improve the conspicuity of small renal cell carcinomas (RCC).
METHODS: Patients were analysed from our institution's pathology-confirmed RCC database that included the following: (1) stage T1a RCCs, (2) an unenhanced computed tomography (CT) abdomen performed ≤ 6 months before histologic diagnosis, and (3) age ≥ 17 years. Patients with multiple tumours, prior nephrectomy, von Hippel-Lindau disease, and polycystic kidney disease were excluded. The unenhanced CT was analysed, and the tumour locations were confirmed by using corresponding contrast-enhanced CT or magnetic resonance imaging studies. Representative single-slice axial, coronal, and sagittal unenhanced CT images were acquired in "soft tissue windows" (width, 400 Hounsfield unit (HU); level, 40 HU) and liver windows (width, 150 HU; level, 88 HU). In addition, single-slice axial, coronal, and sagittal unenhanced CT images of nontumourous renal tissue (obtained from the same cases) were acquired in soft tissue windows and liver windows. These data sets were randomized, unpaired, and were presented independently to 3 blinded radiologists for analysis. The presence or absence of suspicious findings for tumour was scored on a 5-point confidence scale.
RESULTS: Eighty-three of 415 patients met the study criteria. Receiver operating characteristics (ROC) analysis, t test analysis, and kappa analysis were used. ROC analysis showed statistically superior diagnostic performance for liver windows compared with soft tissue windows (area under the curve of 0.923 vs 0.879; P = .0002). Kappa statistics showed "good" vs "moderate" agreement between readers for liver windows compared with soft tissue windows.
CONCLUSION: Use of liver windows settings improves the detection of small RCCs on the unenhanced CT.
Copyright © 2014 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Carcinoma; Diagnostic errors; Diagnostic imaging; Early detection of cancer; Multidetector computed tomography; renal cell

Mesh:

Year:  2013        PMID: 23706868     DOI: 10.1016/j.carj.2012.12.005

Source DB:  PubMed          Journal:  Can Assoc Radiol J        ISSN: 0846-5371            Impact factor:   2.248


  5 in total

1.  Renal cancer at unenhanced CT: imaging features, detection rates, and outcomes.

Authors:  Stacy D O'Connor; Stuart G Silverman; Laila R Cochon; Ramin K Khorasani
Journal:  Abdom Radiol (NY)       Date:  2018-07

2.  Stochastic tissue window normalization of deep learning on computed tomography.

Authors:  Yuankai Huo; Yucheng Tang; Yunqiang Chen; Dashan Gao; Shizhong Han; Shunxing Bao; Smita De; James G Terry; Jeffrey J Carr; Richard G Abramson; Bennett A Landman
Journal:  J Med Imaging (Bellingham)       Date:  2019-11-20

3.  T1a renal cell carcinoma on unenhanced CT: analysis of detectability and imaging features.

Authors:  Aiko Gobara; Takeshi Yoshizako; Rika Yoshida; Megumi Nakamura; Hiroaki Shiina; Hajime Kitagaki
Journal:  Acta Radiol Open       Date:  2019-05-13

4.  Semi-automatic liver segmentation based on probabilistic models and anatomical constraints.

Authors:  Doan Cong Le; Krisana Chinnasarn; Jirapa Chansangrat; Nattawut Keeratibharat; Paramate Horkaew
Journal:  Sci Rep       Date:  2021-03-17       Impact factor: 4.379

Review 5.  Know your way around acute unenhanced CT during global iodinated contrast crisis: a refresher to ED radiologists.

Authors:  Waleed Abdellatif; Vasantha Vasan; Fernando U Kay; Ajay Kohli; Suhny Abbara; Cecelia Brewington
Journal:  Emerg Radiol       Date:  2022-08-10
  5 in total

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