Literature DB >> 32914197

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

Kathleen Nguyen1, Nicola Schieda2, Nick James3, Matthew D F McInnes1, Mark Wu1, Rebecca E Thornhill1.   

Abstract

OBJECTIVE: To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT.
MATERIALS AND METHODS: A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared.
RESULTS: Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05).
CONCLUSION: Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses. KEY POINTS: • Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

Entities:  

Keywords:  Carcinoma; Renal cell; Spiral computed; Tomography

Mesh:

Substances:

Year:  2020        PMID: 32914197     DOI: 10.1007/s00330-020-07233-6

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  40 in total

1.  Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations.

Authors:  Christoph A Karlo; Pier Luigi Di Paolo; Joshua Chaim; A Ari Hakimi; Irina Ostrovnaya; Paul Russo; Hedvig Hricak; Robert Motzer; James J Hsieh; Oguz Akin
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

2.  Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Authors:  Taryn Hodgdon; Matthew D F McInnes; Nicola Schieda; Trevor A Flood; Leslie Lamb; Rebecca E Thornhill
Journal:  Radiology       Date:  2015-04-23       Impact factor: 11.105

3.  Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

Authors:  Zhichao Feng; Pengfei Rong; Peng Cao; Qingyu Zhou; Wenwei Zhu; Zhimin Yan; Qianyun Liu; Wei Wang
Journal:  Eur Radiol       Date:  2017-11-13       Impact factor: 5.315

4.  CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.

Authors:  Siva P Raman; Yifei Chen; James L Schroeder; Peng Huang; Elliot K Fishman
Journal:  Acad Radiol       Date:  2014-09-16       Impact factor: 3.173

5.  Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma.

Authors:  Andrea S Kierans; Henry Rusinek; Andrew Lee; Mohammed B Shaikh; Michael Triolo; William C Huang; Hersh Chandarana
Journal:  AJR Am J Roentgenol       Date:  2014-12       Impact factor: 3.959

6.  Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.

Authors:  Burak Kocak; Emine Sebnem Durmaz; Ece Ates; Melis Baykara Ulusan
Journal:  AJR Am J Roentgenol       Date:  2019-01-02       Impact factor: 3.959

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

Authors:  Nicola Schieda; Robert S Lim; Satheesh Krishna; Matthew D F McInnes; Trevor A Flood; Rebecca E Thornhill
Journal:  AJR Am J Roentgenol       Date:  2018-03-16       Impact factor: 3.959

8.  Small (< 4 cm) Renal Mass: Differentiation of Oncocytoma From Renal Cell Carcinoma on Biphasic Contrast-Enhanced CT.

Authors:  Kohei Sasaguri; Naoki Takahashi; Daniel Gomez-Cardona; Shuai Leng; Grant D Schmit; Rickey E Carter; Bradley C Leibovich; Akira Kawashima
Journal:  AJR Am J Roentgenol       Date:  2015-11       Impact factor: 3.959

Review 9.  CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.

Authors:  Meghan G Lubner; Andrew D Smith; Kumar Sandrasegaran; Dushyant V Sahani; Perry J Pickhardt
Journal:  Radiographics       Date:  2017 Sep-Oct       Impact factor: 5.333

10.  Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis.

Authors:  Camila Lopes Vendrami; Yuri S Velichko; Frank H Miller; Argha Chatterjee; Carolina Parada Villavicencio; Vahid Yaghmai; Robert J McCarthy
Journal:  AJR Am J Roentgenol       Date:  2018-09-21       Impact factor: 3.959

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  1 in total

Review 1.  The Next Paradigm Shift in the Management of Clear Cell Renal Cancer: Radiogenomics-Definition, Current Advances, and Future Directions.

Authors:  Nikhil Gopal; Pouria Yazdian Anari; Evrim Turkbey; Elizabeth C Jones; Ashkan A Malayeri
Journal:  Cancers (Basel)       Date:  2022-02-04       Impact factor: 6.639

  1 in total

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