Literature DB >> 28237280

Multiparametric Magnetic Resonance Imaging of Solid Renal Tumors: A Practical Algorithm.

Francois Cornelis1, Nicolas Grenier2.   

Abstract

Although preoperative classification of solid renal tumors was performed by percutaneous biopsy until now, research teams have demonstrated the potential interest of imaging to characterize noninvasively different renal tumor subtypes, in particular, with multiparametric magnetic resonance (MR) imaging. By combining all the imaging MR features successively reported in the literature and following a practical algorithm based on a step-by-step reading of the MR images, readers are now able to identify several imaging profiles, which appeared specific of each renal tumor subtypes. Although a large, prospective validation remains required to validate these findings in a clinical setting, this new imaging paradigm may help to overcome the traditional limitations of imaging for the characterization of renal tumors because of their overlapped morphologic imaging features. These imaging inputs would be helpful to better identify renal masses requiring surgery, without further invasive exploration such as biopsies, from where other options (ie, percutaneous ablation or active surveillance) may be proposed.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 28237280     DOI: 10.1053/j.sult.2016.08.009

Source DB:  PubMed          Journal:  Semin Ultrasound CT MR        ISSN: 0887-2171            Impact factor:   1.875


  6 in total

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

Authors:  Camila Lopes Vendrami; Robert J McCarthy; Carolina Parada Villavicencio; Frank H Miller
Journal:  Abdom Radiol (NY)       Date:  2020-07-14

2.  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

3.  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 4.  How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score.

Authors:  Ivan Pedrosa; Jeffrey A Cadeddu
Journal:  Radiology       Date:  2021-12-14       Impact factor: 29.146

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

Review 6.  Artificial intelligence for renal cancer: From imaging to histology and beyond.

Authors:  Karl-Friedrich Kowalewski; Luisa Egen; Chanel E Fischetti; Stefano Puliatti; Gomez Rivas Juan; Mark Taratkin; Rivero Belenchon Ines; Marie Angela Sidoti Abate; Julia Mühlbauer; Frederik Wessels; Enrico Checcucci; Giovanni Cacciamani
Journal:  Asian J Urol       Date:  2022-06-18
  6 in total

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