Literature DB >> 23175544

Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer.

Hersh Chandarana1, Andrew B Rosenkrantz, Thais C Mussi, Sooah Kim, Afshan A Ahmad, Sean D Raj, John McMenamy, Jonathan Melamed, James S Babb, Berthold Kiefer, Atilla P Kiraly.   

Abstract

PURPOSE: To compare histogram analysis of voxel-based whole-lesion (WL) enhancement to qualitative assessment and region-of-interest (ROI)-based enhancement analysis in discriminating the renal cell cancer (RCC) subtype clear cell RCC (ccRCC) from papillary RCC (pRCC).
MATERIALS AND METHODS: In this institutional review board-approved, HIPAA-compliant retrospective study, 73 patients underwent magnetic resonance (MR) imaging prior to surgery for RCC between January 2007 and January 2010. Three-dimensional fat-suppressed T1-weighted gradient-echo corticomedullary phase acquisitions, obtained before and after contrast agent administration, were transferred to a workstation at which automated registration followed by semiautomated segmentation of the RCC was performed. Percent enhancement was computed on a per-voxel basis: (SI(post) - SI(pre))/SI(pre) .100, where SI(pre) and SI(post) indicate signal intensity before and after contrast enhancement, respectively. The WL quantitative parameters of mean, median, and third quartile enhancement and histogram distribution parameters kurtosis and skewness were computed for each lesion. WL enhancement parameters were compared with ROI-based analysis and qualitative assessment with regards to diagnostic accuracy and interreader agreement in differentiating ccRCC from pRCC.
RESULTS: There were 19 pRCCs and 55 ccRCCs at pathologic examination. ccRCC had significantly higher WL mean, median, and third quartile enhancement compared with pRCC and hade significantly lower kurtosis and skewness (all P < .001). Third quartile enhancement had the highest accuracy (94.6%; area under the curve, 0.980) in discriminating ccRCC from pRCC, which was significantly higher than the accuracy of qualitative assessment (86.0%; P = .04) but not significantly higher than that of ROI enhancement (89.2%; P = .52). WL enhancement parameters had higher interreader agreement (κ = 0.91-1.0) compared with ROI enhancement or qualitative assessment (κ = 0.83 and 0.7, respectively) in discriminating ccRCC from pRCC.
CONCLUSION: WL enhancement histogram analysis is feasible and can potentially be used to differentiate ccRCC from pRCC with high accuracy. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12111281/-/DC1. © RSNA, 2012.

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Year:  2012        PMID: 23175544     DOI: 10.1148/radiol.12111281

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


  31 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.  Diagnostic Accuracy of MRI for Detection of Papillary Renal Cell Carcinoma: A Systematic Review and Meta-Analysis.

Authors:  Matthew A Chiarello; Rahul D Mali; Stella K Kang
Journal:  AJR Am J Roentgenol       Date:  2018-07-31       Impact factor: 3.959

3.  Fast Temporal Resolution Dynamic Contrast-Enhanced MRI: Histogram Analysis Versus Visual Analysis for Differentiating Benign and Malignant Breast Lesions.

Authors:  Naoko Mori; Federico D Pineda; Keiko Tsuchiya; Shunji Mugikura; Shoki Takahashi; Gregory S Karczmar; Hiroyuki Abe
Journal:  AJR Am J Roentgenol       Date:  2018-07-31       Impact factor: 3.959

Review 4.  Radiomics in Kidney Cancer: MR Imaging.

Authors:  Alberto Diaz de Leon; Payal Kapur; Ivan Pedrosa
Journal:  Magn Reson Imaging Clin N Am       Date:  2019-02       Impact factor: 2.266

5.  Tumor Vascularity in Renal Masses: Correlation of Arterial Spin-Labeled and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Assessments.

Authors:  Yue Zhang; Payal Kapur; Qing Yuan; Yin Xi; Ingrid Carvo; Sabina Signoretti; Ivan Dimitrov; Jeffrey A Cadeddu; Vitaly Margulis; Naira Muradyan; James Brugarolas; Ananth J Madhuranthakam; Ivan Pedrosa
Journal:  Clin Genitourin Cancer       Date:  2015-08-29       Impact factor: 2.872

6.  Comparison of Contrast-Enhanced CT and [18F]FDG PET/CT Analysis Using Kurtosis and Skewness in Patients with Primary Colorectal Cancer.

Authors:  Franca Wagner; Yahya Ali Hakami; Geoffrey Warnock; Gabriel Fischer; Martin W Huellner; Patrick Veit-Haibach
Journal:  Mol Imaging Biol       Date:  2017-10       Impact factor: 3.488

7.  Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma.

Authors:  Yin Xi; Qing Yuan; Yue Zhang; Ananth J Madhuranthakam; Michael Fulkerson; Vitaly Margulis; James Brugarolas; Payal Kapur; Jeffrey A Cadeddu; Ivan Pedrosa
Journal:  Eur Radiol       Date:  2017-07-05       Impact factor: 5.315

8.  Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

Authors:  Gene Young Cho; Linda Moy; Sungheon G Kim; Steven H Baete; Melanie Moccaldi; James S Babb; Daniel K Sodickson; Eric E Sigmund
Journal:  Eur Radiol       Date:  2015-11-28       Impact factor: 5.315

Review 9.  Perfusion computed tomography in renal cell carcinoma.

Authors:  Chandan J Das; Usha Thingujam; Ananya Panda; Sanjay Sharma; Arun Kumar Gupta
Journal:  World J Radiol       Date:  2015-07-28

10.  Differential radiologic characteristics of renal tumours on multiphasic computed tomography.

Authors:  Boon Chye Ching; Hui Shan Tan; Puay Hoon Tan; Chee Keong Toh; Ravindran Kanesvaran; Quan Sing Ng; Min Han Tan
Journal:  Singapore Med J       Date:  2016-04-19       Impact factor: 1.858

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