Literature DB >> 26402495

The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma.

Neema Jamshidi1, Eric Jonasch1, Matthew Zapala1, Ronald L Korn1, Lejla Aganovic1, Hongjuan Zhao1, Raviprakash Tumkur Sitaram1, Robert J Tibshirani1, Sudeep Banerjee1, James D Brooks1, Borje Ljungberg1, Michael D Kuo1.   

Abstract

PURPOSE: To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image.
MATERIALS AND METHODS: In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis.
RESULTS: Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001).
CONCLUSION: A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible. (©) RSNA, 2015 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on August 24, 2015.

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Year:  2015        PMID: 26402495     DOI: 10.1148/radiol.2015150800

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


  24 in total

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Journal:  J Vasc Interv Radiol       Date:  2015-11-25       Impact factor: 3.464

2.  Characterizing recurrent and lethal small renal masses in clear cell renal cell carcinoma using recurrent somatic mutations.

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Review 4.  Imaging genomics in cancer research: limitations and promises.

Authors:  Harrison X Bai; Ashley M Lee; Li Yang; Paul Zhang; Christos Davatzikos; John M Maris; Sharon J Diskin
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5.  An Analysis of Patients with DNA Repair Pathway Mutations Treated with a PARP Inhibitor.

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Journal:  Radiology       Date:  2017-04-28       Impact factor: 11.105

Review 7.  Background, applications and challenges of radiogenomics in genitourinary tumor.

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Journal:  Am J Cancer Res       Date:  2021-05-15       Impact factor: 6.166

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.  The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial.

Authors:  Neema Jamshidi; Eric Jonasch; Matthew Zapala; Ronald L Korn; James D Brooks; Borje Ljungberg; Michael D Kuo
Journal:  Eur Radiol       Date:  2015-11-11       Impact factor: 5.315

10.  Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses.

Authors:  Durgesh Kumar Dwivedi; Yonatan Chatzinoff; Yue Zhang; Qing Yuan; Michael Fulkerson; Rajiv Chopra; James Brugarolas; Jeffrey A Cadeddu; Payal Kapur; Ivan Pedrosa
Journal:  Urology       Date:  2017-10-19       Impact factor: 2.649

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