Literature DB >> 26496547

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

Kohei Sasaguri1,2, Naoki Takahashi1, Daniel Gomez-Cardona1, Shuai Leng1, Grant D Schmit1, Rickey E Carter3, Bradley C Leibovich4, Akira Kawashima1.   

Abstract

OBJECTIVE: The purpose of this study was to evaluate whether small (< 4 cm) oncocytomas can be differentiated from renal cell carcinomas (RCCs) on biphasic contrast-enhanced CT.
MATERIALS AND METHODS: Forty-three patients with 53 oncocytomas and 123 patients with 128 RCCs (24 papillary subtype and 104 clear cell and other subtypes) who underwent biphasic contrast-enhanced CT were included in the study. Patient demographics and CT tumor characteristics were evaluated in each case. A multinomial logistic regression model was then constructed for differentiating oncocytoma from clear cell and other subtype RCCs, oncocytoma from papillary RCCs, and clear cell and other subtype RCCs from papillary RCCs. The probability of each group was calculated from the model. Diagnostic performance among three pairwise diagnoses and between oncocytoma and any RCC (clear cell and other subtypes and papillary) were assessed by AUC values.
RESULTS: Patient age, tumor CT attenuation values and skewness (i.e., histogram analysis of CT values) in both the corticomedullary and nephrographic phases, and subjective tumor heterogeneity were statistically significant variables in the multinomial logistic regression analysis. The logistic regression model using the variables yielded AUCs of 0.82, 0.95, 0.91, and 0.84 for differentiating oncocytomas from clear cell and other subtype RCCs, oncocytomas from papillary RCCs, clear cell and other subtype RCCs from papillary RCCs, and oncocytomas from any RCC (clear cell and other subtypes and papillary), respectively.
CONCLUSION: A combination of imaging features on biphasic CT, including tumor CT attenuation values and tumor texture (heterogeneity and skewness), can help differentiate oncocytoma from RCC.

Entities:  

Keywords:  CT; differential diagnosis; kidney; renal cell carcinoma; renal oncocytoma

Mesh:

Substances:

Year:  2015        PMID: 26496547     DOI: 10.2214/AJR.14.13966

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


  17 in total

1.  Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes.

Authors:  Uyen N Hoang; S Mojdeh Mirmomen; Osorio Meirelles; Jianhua Yao; Maria Merino; Adam Metwalli; W Marston Linehan; Ashkan A Malayeri
Journal:  Abdom Radiol (NY)       Date:  2018-12

2.  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

Review 3.  Imaging features of solid renal masses.

Authors:  Massimo Galia; Domenico Albano; Alberto Bruno; Antonino Agrusa; Giorgio Romano; Giuseppe Di Buono; Francesco Agnello; Giuseppe Salvaggio; Ludovico La Grutta; Massimo Midiri; Roberto Lagalla
Journal:  Br J Radiol       Date:  2017-07-13       Impact factor: 3.039

4.  CT texture analysis of pancreatic cancer.

Authors:  Kumar Sandrasegaran; Yuning Lin; Michael Asare-Sawiri; Tai Taiyini; Mark Tann
Journal:  Eur Radiol       Date:  2018-08-16       Impact factor: 5.315

5.  CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.

Authors:  Yu Deng; Erik Soule; Aster Samuel; Sakhi Shah; Enming Cui; Michael Asare-Sawiri; Chandru Sundaram; Chandana Lall; Kumaresan Sandrasegaran
Journal:  Eur Radiol       Date:  2019-05-24       Impact factor: 5.315

6.  Are growth patterns on MRI in small (< 4 cm) solid renal masses useful for predicting benign histology?

Authors:  Robert S Lim; Matthew D F McInnes; Mahadevaswamy Siddaiah; Trevor A Flood; Luke T Lavallee; Nicola Schieda
Journal:  Eur Radiol       Date:  2018-02-28       Impact factor: 5.315

7.  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

8.  Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat.

Authors:  Zine-Eddine Khene; Karim Bensalah; Axel Largent; Shahrokh Shariat; Gregory Verhoest; Benoit Peyronnet; Oscar Acosta; Renaud DeCrevoisier; Romain Mathieu
Journal:  World J Urol       Date:  2018-04-19       Impact factor: 4.226

9.  Role of Virtual Biopsy in the Management of Renal Masses.

Authors:  Alberto Diaz de Leon; Matthew S Davenport; Stuart G Silverman; Nicola Schieda; Jeffrey A Cadeddu; Ivan Pedrosa
Journal:  AJR Am J Roentgenol       Date:  2019-04-17       Impact factor: 3.959

10.  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

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