Literature DB >> 27595574

Voxel-based whole-lesion enhancement parameters: a study of its clinical value in differentiating clear cell renal cell carcinoma from renal oncocytoma.

Frank Chen1, Mittul Gulati2, Darryl Hwang2, Steven Cen2, Felix Yap2, Chidubem Ugwueze2, Bino Varghese2, Mihir Desai2, Manju Aron2, Inderbir Gill2, Vinay Duddalwar2.   

Abstract

PURPOSE: The purpose of this study was to compare whole-lesion (WL) enhancement parameters to single region of interest (ROI)-based enhancement in discriminating clear cell renal cell carcinoma (ccRCC) from renal oncocytoma.
MATERIALS AND METHODS: In this IRB-approved retrospective study, the surgical database was queried to derive a cohort of 94 postnephrectomy patients with ccRCC or oncocytoma (68 ccRCC, 26 oncocytoma), who underwent preoperative multiphase contrast-enhanced computed tomography (CECT) between June 2009 and August 2013. CT acquisitions were transferred to a three-dimensional workstation, and WL ROIs were manually segmented. WL enhancement and histogram distribution parameters skewness, kurtosis, standard deviation (SD), and interquartile range (IQR) were calculated. WL enhancement parameters were compared to single ROI-based enhancement using receiver operating characteristic (ROC) analysis.
RESULTS: Oncocytoma had significantly higher WL enhancement than ccRCC in nephrographic (mean, p = 0.02; median, p = 0.03) and excretory phases (mean, p = 0.03; median p < 0.01). ccRCC had significantly higher kurtosis than oncocytoma in corticomedullary (p = 0.03) and excretory phases (p < 0.01), and significantly higher SD and IQR than oncocytoma in all postcontrast phases: corticomedullary (SD, p = 0.02; IQR, p < 0.01), nephrographic (SD, p = 0.01; IQR, p = 0.03), and excretory (SD, p < 0.01; IQR, p < 0.01). When compared to single ROI-based enhancement, WL enhancement alone did not demonstrate a statistical advantage in discriminating between ccRCC and oncocytoma (area under ROC curve of 0.78 and 0.72 respectively), but when combined with histogram distribution parameters (area under ROC curve of 0.86), it did demonstrate a slight improvement.
CONCLUSION: Our study suggests that voxel-based WL enhancement parameters provide only a slight improvement over single ROI-based enhancement techniques in differentiating between ccRCC and renal oncocytoma.

Entities:  

Keywords:  CT; Clear cell renal cell carcinoma; Histogram distribution; Oncocytoma; Whole-lesion enhancement

Mesh:

Substances:

Year:  2017        PMID: 27595574     DOI: 10.1007/s00261-016-0891-8

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  7 in total

1.  Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT.

Authors:  Bino A Varghese; Frank Chen; Darryl H Hwang; Steven Y Cen; Inderbir S Gill; Vinay A Duddalwar
Journal:  Br J Radiol       Date:  2018-06-21       Impact factor: 3.039

2.  A Decision-Support Tool for Renal Mass Classification.

Authors:  Gautam Kunapuli; Bino A Varghese; Priya Ganapathy; Bhushan Desai; Steven Cen; Manju Aron; Inderbir Gill; Vinay Duddalwar
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

3.  Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

Authors:  Assad Oberai; Bino Varghese; Steven Cen; Tomas Angelini; Darryl Hwang; Inderbir Gill; Manju Aron; Christopher Lau; Vinay Duddalwar
Journal:  Br J Radiol       Date:  2020-05-11       Impact factor: 3.039

4.  Usefulness of multidetector computed tomography to differentiate between renal cell carcinoma and oncocytoma. A model validation.

Authors:  Blanca Paño; Alexandre Soler; Debra A Goldman; Rafael Salvador; Laura Buñesch; Carmen Sebastià; Carlos Nicolau
Journal:  Br J Radiol       Date:  2020-08-26       Impact factor: 3.039

5.  Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes.

Authors:  Rahul Rajendran; Kevan Iffrig; Deepak K Pruthi; Allison Wheeler; Brian Neuman; Dharam Kaushik; Ahmed M Mansour; Karen Panetta; Sos Agaian; Michael A Liss
Journal:  Adv Urol       Date:  2019-04-23

6.  Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma.

Authors:  Yusuke Uchida; Soichiro Yoshida; Yuki Arita; Hiroki Shimoda; Koichiro Kimura; Ichiro Yamada; Hajime Tanaka; Minato Yokoyama; Yoh Matsuoka; Masahiro Jinzaki; Yasuhisa Fujii
Journal:  Diagnostics (Basel)       Date:  2022-03-26

Review 7.  Artificial intelligence for renal cancer: From imaging to histology and beyond.

Authors:  Karl-Friedrich Kowalewski; Luisa Egen; Chanel E Fischetti; Stefano Puliatti; Gomez Rivas Juan; Mark Taratkin; Rivero Belenchon Ines; Marie Angela Sidoti Abate; Julia Mühlbauer; Frederik Wessels; Enrico Checcucci; Giovanni Cacciamani
Journal:  Asian J Urol       Date:  2022-06-18
  7 in total

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