Literature DB >> 28187264

Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer.

Alice C Yu1, Chaitra Badve1, Lee E Ponsky1, Shivani Pahwa1, Sara Dastmalchian1, Matthew Rogers1, Yun Jiang1, Seunghee Margevicius1, Mark Schluchter1, William Tabayoyong1, Robert Abouassaly1, Debra McGivney1, Mark A Griswold1, Vikas Gulani1.   

Abstract

Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10-3 mm2/sec ± 0.331, respectively) were significantly lower than those from NPZ (mean, 2247 msec ± 450, 169 msec ± 61, and 1.711 × 10-3 mm2/sec ± 0.269) (P < .0001 for each) and together produced the best separation between these groups (AUC = 0.99). ADC and T2 together produced the highest AUC of 0.83 for separating high- or intermediate-grade tumors from low-grade cancers. T1, T2, and ADC in prostatitis (mean, 1707 msec ± 377, 79 msec ± 37, and 0.911 × 10-3 mm2/sec ± 0.239) were significantly lower than those in NPZ (P < .0005 for each). Interreader agreement was excellent, with an intraclass correlation coefficient greater than 0.75 for both T1 and T2 measurements. Conclusion This study describes the development of a rapid MR fingerprinting- and diffusion-based examination for quantitative characterization of prostatic tissue. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28187264      PMCID: PMC5452885          DOI: 10.1148/radiol.2017161599

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


  32 in total

1.  MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout.

Authors:  Yun Jiang; Dan Ma; Nicole Seiberlich; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2014-12-09       Impact factor: 4.668

2.  Characterization of prostate cancer using T2 mapping at 3T: a multi-scanner study.

Authors:  A Hoang Dinh; R Souchon; C Melodelima; F Bratan; F Mège-Lechevallier; M Colombel; O Rouvière
Journal:  Diagn Interv Imaging       Date:  2014-12-23       Impact factor: 4.026

3.  Differentiation of prostatitis and prostate cancer by using diffusion-weighted MR imaging and MR-guided biopsy at 3 T.

Authors:  Klaas N A Nagel; Martijn G Schouten; Thomas Hambrock; Geert J S Litjens; Caroline M A Hoeks; Bennie ten Haken; Jelle O Barentsz; Jurgen J Fütterer
Journal:  Radiology       Date:  2013-01-17       Impact factor: 11.105

4.  Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.

Authors:  M Minhaj Siddiqui; Soroush Rais-Bahrami; Baris Turkbey; Arvin K George; Jason Rothwax; Nabeel Shakir; Chinonyerem Okoro; Dima Raskolnikov; Howard L Parnes; W Marston Linehan; Maria J Merino; Richard M Simon; Peter L Choyke; Bradford J Wood; Peter A Pinto
Journal:  JAMA       Date:  2015-01-27       Impact factor: 56.272

5.  Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence.

Authors:  Fernando I Yamauchi; Tobias Penzkofer; Andriy Fedorov; Fiona M Fennessy; Renxin Chu; Stephan E Maier; Clare M C Tempany; Robert V Mulkern; Lawrence P Panych
Journal:  Magn Reson Imaging       Date:  2015-02-14       Impact factor: 2.546

6.  Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer.

Authors:  Eline K Vos; Thiele Kobus; Geert J S Litjens; Thomas Hambrock; Christina A Hulsbergen-van de Kaa; Jelle O Barentsz; Marnix C Maas; Tom W J Scheenen
Journal:  Invest Radiol       Date:  2015-08       Impact factor: 6.016

7.  Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer.

Authors:  Thomas Hambrock; Diederik M Somford; Henkjan J Huisman; Inge M van Oort; J Alfred Witjes; Christina A Hulsbergen-van de Kaa; Thomas Scheenen; Jelle O Barentsz
Journal:  Radiology       Date:  2011-05       Impact factor: 11.105

8.  Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.

Authors:  Yahui Peng; Yulei Jiang; Cheng Yang; Jeremy Bancroft Brown; Tatjana Antic; Ila Sethi; Christine Schmid-Tannwald; Maryellen L Giger; Scott E Eggener; Aytekin Oto
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

9.  Magnetic resonance fingerprinting.

Authors:  Dan Ma; Vikas Gulani; Nicole Seiberlich; Kecheng Liu; Jeffrey L Sunshine; Jeffrey L Duerk; Mark A Griswold
Journal:  Nature       Date:  2013-03-14       Impact factor: 49.962

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  47 in total

1.  Three-dimensional simultaneous brain T1 , T2 , and ADC mapping with MR Multitasking.

Authors:  Sen Ma; Christopher T Nguyen; Fei Han; Nan Wang; Zixin Deng; Nader Binesh; Franklin G Moser; Anthony G Christodoulou; Debiao Li
Journal:  Magn Reson Med       Date:  2019-11-25       Impact factor: 4.668

Review 2.  Magnetic resonance fingerprinting: an overview.

Authors:  Charit Tippareddy; Walter Zhao; Jeffrey L Sunshine; Mark Griswold; Dan Ma; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-26       Impact factor: 9.236

3.  Three-dimensional MR Fingerprinting for Quantitative Breast Imaging.

Authors:  Yong Chen; Ananya Panda; Shivani Pahwa; Jesse I Hamilton; Sara Dastmalchian; Debra F McGivney; Dan Ma; Joshua Batesole; Nicole Seiberlich; Mark A Griswold; Donna Plecha; Vikas Gulani
Journal:  Radiology       Date:  2018-10-30       Impact factor: 11.105

4.  Magnetic resonance fingerprinting of the pancreas at 1.5 T and 3.0 T.

Authors:  Eva M Serrao; Dimitri A Kessler; Bruno Carmo; Lucian Beer; Kevin M Brindle; Guido Buonincontri; Ferdia A Gallagher; Fiona J Gilbert; Edmund Godfrey; Martin J Graves; Mary A McLean; Evis Sala; Rolf F Schulte; Joshua D Kaggie
Journal:  Sci Rep       Date:  2020-10-16       Impact factor: 4.379

5.  Multi-frequency interpolation in spiral magnetic resonance fingerprinting for correction of off-resonance blurring.

Authors:  Jason Ostenson; Ryan K Robison; Nicholas R Zwart; E Brian Welch
Journal:  Magn Reson Imaging       Date:  2017-07-08       Impact factor: 2.546

6.  MR fingerprinting for rapid simultaneous T1 , T2 , and T1 ρ relaxation mapping of the human articular cartilage at 3T.

Authors:  Azadeh Sharafi; Marcelo V W Zibetti; Gregory Chang; Martijn Cloos; Ravinder R Regatte
Journal:  Magn Reson Med       Date:  2020-05-09       Impact factor: 4.668

Review 7.  Magnetic resonance fingerprinting review part 2: Technique and directions.

Authors:  Debra F McGivney; Rasim Boyacıoğlu; Yun Jiang; Megan E Poorman; Nicole Seiberlich; Vikas Gulani; Kathryn E Keenan; Mark A Griswold; Dan Ma
Journal:  J Magn Reson Imaging       Date:  2019-07-25       Impact factor: 4.813

8.  Stimulated echo based mapping (STEM) of T1 , T2 , and apparent diffusion coefficient: validation and protocol optimization.

Authors:  Yuxin Zhang; Shane A Wells; Diego Hernando
Journal:  Magn Reson Med       Date:  2018-07-19       Impact factor: 4.668

9.  Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting.

Authors:  Johannes Haubold; Aydin Demircioglu; Marcel Gratz; Martin Glas; Karsten Wrede; Ulrich Sure; Gerald Antoch; Kathy Keyvani; Mathias Nittka; Stephan Kannengiesser; Vikas Gulani; Mark Griswold; Ken Herrmann; Michael Forsting; Felix Nensa; Lale Umutlu
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-06       Impact factor: 9.236

Review 10.  New prostate MRI techniques and sequences.

Authors:  Aritrick Chatterjee; Carla Harmath; Aytekin Oto
Journal:  Abdom Radiol (NY)       Date:  2020-12
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