Literature DB >> 23971479

Prostate cancer: comparison of dynamic contrast-enhanced MRI techniques for localization of peripheral zone tumor.

Andrew B Rosenkrantz1, Amy Sabach, James S Babb, Brent W Matza, Samir S Taneja, Fang-Ming Deng.   

Abstract

OBJECTIVE: The objective of this study was to compare the performance of different methodologies for interpretation of dynamic contrast-enhanced MRI (DCE-MRI) in localization of peripheral zone prostate cancer.
MATERIALS AND METHODS: Forty-three men (mean age, 59±8 years) with biopsy-proven prostate cancer who underwent prostate MRI including DCE-MRI before prostatectomy were included. Two observers independently reviewed DCE-MRI data using three methodologies: qualitative, in which kinetic curves of signal intensity versus time were generated for foci showing rapid enhancement on subtracted contrast-enhanced images; semiquantitative, in which a biexponential heuristic model was used to generate color-coded maps depicting maximum slope and washout of contrast enhancement; and quantitative, in which a Tofts model was used to generate color-coded influx rate transfer constant (Ktrans) and efflux rate transfer constant (Kep) maps. Findings were stratified by whether suspicious foci showed evidence of washout with each method and compared with histopathologic results in each sextant.
RESULTS: There was similar accuracy for the semiquantitative and quantitative models for both observers irrespective of requiring evidence of washout. For the more experienced observer, requiring washout resulted in lower sensitivity and higher specificity for the qualitative and semiquantitative models. Also for the more experienced observer, use of either a semiquantitative or quantitative model provided greater sensitivity compared with a qualitative model when requiring washout. There was no association between tumor detection and Gleason score for any DCE-MRI methodology for either reader.
CONCLUSION: For the experienced reader, sensitivity for peripheral zone tumor was increased by use of either a semiquantitative or quantitative model compared with a qualitative model and decreased by requiring washout. We failed to identify a difference in performance between semiquantitative and quantitative models.

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Year:  2013        PMID: 23971479     DOI: 10.2214/AJR.12.9737

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


  7 in total

1.  Dynamic contrast-enhanced MRI of the prostate: An intraindividual assessment of the effect of temporal resolution on qualitative detection and quantitative analysis of histopathologically proven prostate cancer.

Authors:  Justin M Ream; Ankur M Doshi; Diane Dunst; Nainesh Parikh; Max X Kong; James S Babb; Samir S Taneja; Andrew B Rosenkrantz
Journal:  J Magn Reson Imaging       Date:  2016-09-20       Impact factor: 4.813

2.  Correlation between dynamic contrast-enhanced MRI and quantitative histopathologic microvascular parameters in organ-confined prostate cancer.

Authors:  Cornelis G van Niekerk; Jeroen A W M van der Laak; Thomas Hambrock; Henk-Jan Huisman; J Alfred Witjes; Jelle O Barentsz; Christina A Hulsbergen-van de Kaa
Journal:  Eur Radiol       Date:  2014-07-18       Impact factor: 5.315

3.  MR-sequences for prostate cancer diagnostics: validation based on the PI-RADS scoring system and targeted MR-guided in-bore biopsy.

Authors:  Lars Schimmöller; Michael Quentin; Christian Arsov; Andreas Hiester; Christian Buchbender; Robert Rabenalt; Peter Albers; Gerald Antoch; Dirk Blondin
Journal:  Eur Radiol       Date:  2014-06-28       Impact factor: 5.315

4.  Performance of PI-RADS version 1 versus version 2 regarding the relation with histopathological results.

Authors:  Thomas Auer; Michael Edlinger; Jasmin Bektic; Udo Nagele; Thomas Herrmann; Georg Schäfer; Friedrich Aigner; Daniel Junker
Journal:  World J Urol       Date:  2016-08-10       Impact factor: 4.226

5.  Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

Authors:  David Jean Winkel; Hanns-Christian Breit; Bibo Shi; Daniel T Boll; Hans-Helge Seifert; Christian Wetterauer
Journal:  Quant Imaging Med Surg       Date:  2020-04

6.  Biparametric versus multiparametric MRI in the diagnosis of prostate cancer.

Authors:  Karen Cecilie Duus Thestrup; Vibeke Logager; Ingerd Baslev; Jakob M Møller; Rasmus Hvass Hansen; Henrik S Thomsen
Journal:  Acta Radiol Open       Date:  2016-08-17

7.  Role of dynamic perfusion magnetic resonance imaging in patients with local advanced rectal cancer.

Authors:  Davide Ippolito; Silvia Girolama Drago; Anna Pecorelli; Cesare Maino; Giulia Querques; Ilaria Mariani; Cammillo Talei Franzesi; Sandro Sironi
Journal:  World J Gastroenterol       Date:  2020-05-28       Impact factor: 5.742

  7 in total

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