Literature DB >> 28095036

Differentiation of Clear Cell Renal Cell Carcinoma From Other Renal Cortical Tumors by Use of a Quantitative Multiparametric MRI Approach.

Andreas M Hötker1,2, Yousef Mazaheri3, Andreas Wibmer1, Christoph A Karlo1, Junting Zheng4, Chaya S Moskowitz4, Satish K Tickoo5, Paul Russo6, Hedvig Hricak1, Oguz Akin1.   

Abstract

OBJECTIVE: The purpose of this study was to develop a quantitative multiparametric MRI approach to differentiating clear cell renal cell carcinoma (RCC) from other renal cortical tumors.
MATERIALS AND METHODS: This retrospective study included 119 patients with 124 histopathologically confirmed renal cortical tumors who underwent preoperative MRI including DWI, contrast-enhanced, and chemical-shift sequences before nephrectomy. Two radiologists independently assessed each tumor volumetrically, and apparent diffusion coefficient values, parameters from multiphasic contrast-enhanced MRI (peak enhancement, upslope, downslope, AUC), and chemical-shift indexes were calculated. Univariate and multivariable logistic regression analyses were performed to identify parameters associated with clear cell RCC.
RESULTS: Interreader agreement was excellent (intraclass correlation coefficient, 0.815-0.994). The parameters apparent diffusion coefficient (reader 1 AUC, 0.804; reader 2, 0.807), peak enhancement (reader 1 AUC, 0.629; reader 2, 0.606), and downslope (reader 1 AUC, 0.575; reader 2, 0.561) were significantly associated with discriminating clear cell RCC from other renal cortical tumors. The combination of all three parameters further increased diagnostic accuracy (reader 1 AUC, 0.889; reader 2, 0.907; both p ≤ 0.001), yielding sensitivities of 0.897 for reader 1 and 0.897 for reader 2, and specificities of 0.762 for reader 1 and 0.738 for reader 2 in the identification of clear cell RCC. With maximized sensitivity, specificities of 0.429 and 0.262 were reached for readers 1 and 2, respectively.
CONCLUSION: A quantitative multiparametric approach statistically significantly improves diagnostic performance in differentiating clear cell RCC from other renal cortical tumors.

Entities:  

Keywords:  DWI; MRI; diagnostic imaging; diffusion; kidney; neoplasms; renal cell carcinoma

Mesh:

Year:  2017        PMID: 28095036      PMCID: PMC5441564          DOI: 10.2214/AJR.16.16652

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


  24 in total

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2.  Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma.

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4.  Testing for improvement in prediction model performance.

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5.  NIH Image to ImageJ: 25 years of image analysis.

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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
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7.  Renal cell carcinoma: diffusion-weighted MR imaging for subtype differentiation at 3.0 T.

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8.  Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT.

Authors:  Jonathan R Young; Daniel Margolis; Steven Sauk; Allan J Pantuck; James Sayre; Steven S Raman
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9.  Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings.

Authors:  Rotem S Lanzman; Phil M Robson; Maryellen R Sun; Amish D Patel; Kimiknu Mentore; Andrew A Wagner; Elizabeth M Genega; Neil M Rofsky; David C Alsop; Ivan Pedrosa
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10.  Renal oncocytoma: CT features cannot reliably distinguish oncocytoma from other renal neoplasms.

Authors:  S Choudhary; A Rajesh; N J Mayer; K A Mulcahy; A Haroon
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  13 in total

1.  Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics.

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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.  Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.

Authors:  Zhi-Cheng Li; Guangtao Zhai; Jinheng Zhang; Zhongqiu Wang; Guiqin Liu; Guang-Yu Wu; Dong Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-12-06       Impact factor: 5.315

4.  Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT-correlation with microvascular density.

Authors:  Julian Marcon; Anno Graser; David Horst; Jozefina Casuscelli; Annabel Spek; Christian G Stief; Maximilian F Reiser; Johannes Rübenthaler; Alexander Buchner; Michael Staehler
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5.  Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging to Identify Clear Cell Renal Cell Carcinoma in cT1a Renal Masses.

Authors:  Noah E Canvasser; Fernando U Kay; Yin Xi; Daniella F Pinho; Daniel Costa; Alberto Diaz de Leon; Gaurav Khatri; John R Leyendecker; Takeshi Yokoo; Aaron Lay; Nicholas Kavoussi; Ersin Koseoglu; Jeffrey A Cadeddu; Ivan Pedrosa
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6.  Capability of intravoxel incoherent motion and diffusion tensor imaging to detect early kidney injury in type 2 diabetes.

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

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Review 9.  [Biopsies of kidney lesions: when and how?]

Authors:  A H Schuster; N Reimann
Journal:  Radiologe       Date:  2018-10       Impact factor: 0.635

10.  Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging.

Authors:  Ryan L Steinberg; Robert G Rasmussen; Brett A Johnson; Rashed Ghandour; Alberto Diaz De Leon; Yin Xi; Takeshi Yokoo; Sandy Kim; Payal Kapur; Jeffrey A Cadeddu; Ivan Pedrosa
Journal:  Eur Radiol       Date:  2020-08-08       Impact factor: 5.315

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