Literature DB >> 26919441

Homogeneous T1 Hyperintense Renal Lesions with Smooth Borders: Is Contrast-enhanced MR Imaging Needed?

Amir H Davarpanah1, Michael Spektor1, Mahan Mathur1, Gary M Israel1.   

Abstract

Purpose To retrospectively determine if homogeneous high T1 signal intensity (SI) masses with smooth borders on unenhanced magnetic resonance (MR) images can be characterized as benign. Materials and Methods Institutional review board approval was obtained for this HIPAA-compliant retrospective study, with waiver of informed consent. MR images in 84 patients with hemorrhagic or proteinaceous cysts and 50 patients with renal cell carcinoma (RCC) were evaluated. Sixty-three cysts and 49 RCCs underwent unenhanced computed tomography (CT). SI ratio and CT attenuation were determined. Two radiologists evaluated lesions as follows: score 1, homogeneous with smooth borders; score 2, mildly heterogeneous with mildly lobulated borders; score 3, moderately heterogeneous and irregular borders; and score 4, markedly heterogeneous with markedly irregular borders. Statistical analysis was performed by using multivariable logistic regression, Welch t test, Z test, Fisher-exact test, Shapiro-Wilk test, and receiver operating characteristic curve analysis. A diagnostic criterion was formulated by using classification and regression tree analysis. Results SI ratio and attenuation of hemorrhagic or proteinaceous cysts were significantly higher than those of RCCs (SI ratio: cyst 2.4 ± 0.8, RCC 1.5 ± 0.3; attenuation: cyst 51.9 ± 21.5, RCC: 34.8 ± 10.0). Reader 1 scored morphology of 68 (81%) hemorrhagic or proteinaceous cysts as score 1 on MR images and as score 45 (71%) on CT scans. Reader 2 scored morphology of 59 (70%) hemorrhagic or proteinaceous cysts as score 1 on MR images and as score 43 (68%) on CT scans. Two-step classification tree suggested that homogeneous high T1 SI lesions with smooth borders and SI ratio of greater than 1.6 predict the lesion as benign cysts. Similar algorithm for CT suggested threshold of 51 HU. Increasing threshold to 2.5 for SI ratio and 66 for Hounsfield units resulted in 99.9% confidence for characterizing benign cysts. Conclusion The retrospective assessment shows that morphologic assessment and SI quantification on unenhanced T1-weighted MR images can be used to differentiate benign hemorrhagic or proteinaceous cysts from RCC, although prospective assessment will be needed to confirm these results. (©) RSNA, 2016.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 26919441     DOI: 10.1148/radiol.16151240

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


  4 in total

Review 1.  Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment.

Authors:  Stuart G Silverman; Ivan Pedrosa; James H Ellis; Nicole M Hindman; Nicola Schieda; Andrew D Smith; Erick M Remer; Atul B Shinagare; Nicole E Curci; Steven S Raman; Shane A Wells; Samuel D Kaffenberger; Zhen J Wang; Hersh Chandarana; Matthew S Davenport
Journal:  Radiology       Date:  2019-06-18       Impact factor: 11.105

2.  Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

Authors:  Felix Y Yap; Bino A Varghese; Steven Y Cen; Darryl H Hwang; Xiaomeng Lei; Bhushan Desai; Christopher Lau; Lindsay L Yang; Austin J Fullenkamp; Simin Hajian; Marielena Rivas; Megha Nayyar Gupta; Brian D Quinn; Manju Aron; Mihir M Desai; Monish Aron; Assad A Oberai; Inderbir S Gill; Vinay A Duddalwar
Journal:  Eur Radiol       Date:  2020-08-15       Impact factor: 5.315

3.  Solid renal masses in adults.

Authors:  Mahesh Kumar Mittal; Binit Sureka
Journal:  Indian J Radiol Imaging       Date:  2016 Oct-Dec

Review 4.  Update on MRI of Cystic Renal Masses Including Bosniak Version 2019.

Authors:  Satheesh Krishna; Nicola Schieda; Ivan Pedrosa; Nicole Hindman; Ronaldo H Baroni; Stuart G Silverman; Matthew S Davenport
Journal:  J Magn Reson Imaging       Date:  2020-10-02       Impact factor: 4.813

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.