Literature DB >> 29547054

Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma.

Nicola Schieda1, Robert S Lim2, Satheesh Krishna1, Matthew D F McInnes1, Trevor A Flood2, Rebecca E Thornhill1.   

Abstract

OBJECTIVE: The objective of our study was to evaluate tumor attenuation and texture on unenhanced CT for potential differentiation of low-grade from high-grade chromophobe renal cell carcinoma (RCC).
MATERIALS AND METHODS: A retrospective study of 37 consecutive patients with chromophobe RCC (high-grade, n = 13; low-grade, n = 24) who underwent preoperative unenhanced CT between 2011 and 2016 was performed. Two radiologists (readers 1 and 2) blinded to the histologic grade of the tumor and outcome of the patients subjectively evaluated tumor homogeneity (3-point scale: completely homogeneous, mildly heterogeneous, or mostly heterogeneous). A third radiologist, also blinded to tumor grade and patient outcome, measured attenuation and contoured tumors for quantitative texture analysis. Comparisons were performed between high-grade and low-grade tumors using the chi-square test for subjective variables and sex, independent t tests for patient age and tumor attenuation, and Mann-Whitney U tests for texture analysis. Logistic regression models and ROC curves were computed.
RESULTS: There were no differences in age or sex between the groups (p = 0.652 and 0.076). High-grade tumors were larger (mean ± SD, 62.6 ± 34.9 mm [range, 17.0-141.0 mm] vs 39.0 ± 17.9 mm [16.0-72.3 mm]; p = 0.009) and had higher attenuation (mean ± SD, 45.5 ± 8.2 HU [range, 29.0-55.0 HU] vs 35.3 ± 8.5 HU [14.0-51.0 HU]; p = 0.001) than low-grade tumors. CT size and attenuation achieved good accuracy to diagnose high-grade chromophobe RCC: The AUC ± standard error was 0.85 ± 0.08 (p < 0.0001) with a sensitivity of 69.0% and a specificity of 100%. Subjectively, high-grade tumors were more heterogeneous (mildly or markedly heterogeneous: 69.2% [9/13] for reader 1 and 76.9% [10/13] for reader 2; reader 1, p = 0.024; reader 2, p = 0.001) with moderate agreement (κ = 0.57). Combined texture features diagnosed high-grade tumors with a maximal AUC of 0.84 ± 0.06 (p < 0.0001).
CONCLUSION: Tumor attenuation and heterogeneity assessed on unenhanced CT are associated with high-grade chromophobe RCC and correlate well with the histopathologic chromophobe tumor grading system.

Entities:  

Keywords:  CT; chromophobe; grade; renal cell carcinoma; texture analysis

Mesh:

Year:  2018        PMID: 29547054     DOI: 10.2214/AJR.17.18874

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  12 in total

1.  Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.

Authors:  Burak Kocak; Ece Ates; Emine Sebnem Durmaz; Melis Baykara Ulusan; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2019-02-12       Impact factor: 5.315

Review 2.  An overview of non-invasive imaging modalities for diagnosis of solid and cystic renal lesions.

Authors:  Ravinder Kaur; Mamta Juneja; A K Mandal
Journal:  Med Biol Eng Comput       Date:  2019-11-21       Impact factor: 2.602

3.  Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT.

Authors:  Nicola Schieda; Kathleen Nguyen; Rebecca E Thornhill; Matthew D F McInnes; Mark Wu; Nick James
Journal:  Abdom Radiol (NY)       Date:  2020-07-05

Review 4.  CT-based radiomics for differentiating renal tumours: a systematic review.

Authors:  Abhishta Bhandari; Muhammad Ibrahim; Chinmay Sharma; Rebecca Liong; Sonja Gustafson; Marita Prior
Journal:  Abdom Radiol (NY)       Date:  2020-11-02

Review 5.  [Noninvasive phenotyping of renal tumors-current state of the art].

Authors:  Hubert Stefan Bickel
Journal:  Radiologe       Date:  2018-10       Impact factor: 0.635

6.  Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.

Authors:  Kathleen Nguyen; Nicola Schieda; Nick James; Matthew D F McInnes; Mark Wu; Rebecca E Thornhill
Journal:  Eur Radiol       Date:  2020-09-10       Impact factor: 5.315

7.  Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

Authors:  Ceyda Turan Bektas; Burak Kocak; Aytul Hande Yardimci; Mehmet Hamza Turkcanoglu; Ugur Yucetas; Sevim Baykal Koca; Cagri Erdim; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2018-08-30       Impact factor: 5.315

8.  Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis.

Authors:  Wen Jing Cui; Cheng Wang; Ling Jia; Shuai Ren; Shao Feng Duan; Can Cui; Xiao Chen; Zhong Qiu Wang
Journal:  Front Oncol       Date:  2019-05-29       Impact factor: 6.244

9.  Pancreatic Ductal Adenocarcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis For Prediction Of Histopathological Grade.

Authors:  Wenli Qiu; Na Duan; Xiao Chen; Shuai Ren; Yifen Zhang; Zhongqiu Wang; Rong Chen
Journal:  Cancer Manag Res       Date:  2019-10-30       Impact factor: 3.989

10.  Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.

Authors:  Zihua Wang; Yufang He; Nianhua Wang; Ting Zhang; Hongzhen Wu; Xinqing Jiang; Lei Mo
Journal:  Medicine (Baltimore)       Date:  2020-05       Impact factor: 1.817

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