Literature DB >> 29390196

Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology.

Fernando U Kay1, Noah E Canvasser1, Yin Xi1, Daniella F Pinho1, Daniel N Costa1, Alberto Diaz de Leon1, Gaurav Khatri1, John R Leyendecker1, Takeshi Yokoo1, Aaron H Lay1, Nicholas Kavoussi1, Ersin Koseoglu1, Jeffrey A Cadeddu1, Ivan Pedrosa1.   

Abstract

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29390196      PMCID: PMC5929366          DOI: 10.1148/radiol.2018171557

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  29 in total

1.  MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis.

Authors:  Nicola Schieda; Marc Dilauro; Bardia Moosavi; Taryn Hodgdon; Gregory O Cron; Matthew D F McInnes; Trevor A Flood
Journal:  Eur Radiol       Date:  2015-10-20       Impact factor: 5.315

2.  MR classification of renal masses with pathologic correlation.

Authors:  Ivan Pedrosa; Mary T Chou; Long Ngo; Ronaldo H Baroni; Elizabeth M Genega; Laura Galaburda; William C DeWolf; Neil M Rofsky
Journal:  Eur Radiol       Date:  2007-09-26       Impact factor: 5.315

3.  Active surveillance for renal cortical neoplasms.

Authors:  Juan Carlos Rosales; Georgios Haramis; Jorge Moreno; Ketan Badani; Mitchell C Benson; James McKiernan; Cristin Casazza; Jaime Landman
Journal:  J Urol       Date:  2010-03-17       Impact factor: 7.450

4.  Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists.

Authors:  Andrew B Rosenkrantz; Luke A Ginocchio; Daniel Cornfeld; Adam T Froemming; Rajan T Gupta; Baris Turkbey; Antonio C Westphalen; James S Babb; Daniel J Margolis
Journal:  Radiology       Date:  2016-04-01       Impact factor: 11.105

5.  Rising incidence of small renal masses: a need to reassess treatment effect.

Authors:  John M Hollingsworth; David C Miller; Stephanie Daignault; Brent K Hollenbeck
Journal:  J Natl Cancer Inst       Date:  2006-09-20       Impact factor: 13.506

6.  Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques?

Authors:  Nicole Hindman; Long Ngo; Elizabeth M Genega; Jonathan Melamed; Jesse Wei; Julia M Braza; Neil M Rofsky; Ivan Pedrosa
Journal:  Radiology       Date:  2012-09-25       Impact factor: 11.105

Review 7.  Increased incidence of serendipitously discovered renal cell carcinoma.

Authors:  M Jayson; H Sanders
Journal:  Urology       Date:  1998-02       Impact factor: 2.649

Review 8.  DWI for Renal Mass Characterization: Systematic Review and Meta-Analysis of Diagnostic Test Performance.

Authors:  Stella K Kang; Angela Zhang; Pari V Pandharipande; Hersh Chandarana; R Scott Braithwaite; Benjamin Littenberg
Journal:  AJR Am J Roentgenol       Date:  2015-08       Impact factor: 3.959

9.  Segmental enhancement inversion at biphasic multidetector CT: characteristic finding of small renal oncocytoma.

Authors:  Jung Im Kim; Jeong Yeon Cho; Kyung Chul Moon; Hak Jong Lee; Seung Hyup Kim
Journal:  Radiology       Date:  2009-06-09       Impact factor: 11.105

Review 10.  Preoperatively misclassified, surgically removed benign renal masses: a systematic review of surgical series and United States population level burden estimate.

Authors:  David C Johnson; Josip Vukina; Angela B Smith; Anne-Marie Meyer; Stephanie B Wheeler; Tzy-Mey Kuo; Hung-Jui Tan; Michael E Woods; Mathew C Raynor; Eric M Wallen; Raj S Pruthi; Matthew E Nielsen
Journal:  J Urol       Date:  2014-07-27       Impact factor: 7.450

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  16 in total

1.  Society of Abdominal Radiology disease-focused panel on renal cell carcinoma: update on past, current, and future goals.

Authors:  Matthew S Davenport; Hersh Chandarana; Nicole E Curci; Ankur Doshi; Samuel D Kaffenberger; Ivan Pedrosa; Erick M Remer; Nicola Schieda; Atul B Shinagare; Andrew D Smith; Zhen J Wang; Shane A Wells; Stuart G Silverman
Journal:  Abdom Radiol (NY)       Date:  2018-09

2.  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

3.  Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease-focused panel on renal cell carcinoma.

Authors:  Nicola Schieda; Matthew S Davenport; Ivan Pedrosa; Atul Shinagare; Hersch Chandarana; Nicole Curci; Ankur Doshi; Gary Israel; Erick Remer; Jane Wang; Stuart G Silverman
Journal:  J Magn Reson Imaging       Date:  2019-01-28       Impact factor: 4.813

4.  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

Review 5.  Imaging of Solid Renal Masses.

Authors:  Fernando U Kay; Ivan Pedrosa
Journal:  Urol Clin North Am       Date:  2018-06-15       Impact factor: 2.241

6.  Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging.

Authors:  Brett A Johnson; Sandy Kim; Ryan L Steinberg; Alberto Diaz de Leon; Ivan Pedrosa; Jeffrey A Cadeddu
Journal:  Urol Oncol       Date:  2019-09-17       Impact factor: 3.498

7.  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

8.  Lexicon for renal mass terms at CT and MRI: a consensus of the society of abdominal radiology disease-focused panel on renal cell carcinoma.

Authors:  Atul B Shinagare; Matthew S Davenport; Hyesun Park; Ivan Pedrosa; Erick M Remer; Hersh Chandarana; Ankur M Doshi; Nicola Schieda; Andrew D Smith; Raghunandan Vikram; Zhen J Wang; Stuart G Silverman
Journal:  Abdom Radiol (NY)       Date:  2020-08-18

9.  Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

Authors:  Shawn Haji-Momenian; Zixian Lin; Bhumi Patel; Nicole Law; Adam Michalak; Anishsanjay Nayak; James Earls; Murray Loew
Journal:  Abdom Radiol (NY)       Date:  2020-03

Review 10.  Imaging Advances in the Management of Kidney Cancer.

Authors:  Katherine M Krajewski; Ivan Pedrosa
Journal:  J Clin Oncol       Date:  2018-10-29       Impact factor: 44.544

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