Literature DB >> 25683515

Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation.

Fiona M Fennessy1, Andriy Fedorov2, Tobias Penzkofer3, Kyung Won Kim4, Michelle S Hirsch5, Mark G Vangel6, Paul Masry5, Trevor A Flood5, Ming-Ching Chang7, Clare M Tempany2, Robert V Mulkern8, Sandeep N Gupta7.   

Abstract

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Arterial input function; Dynamic contrast enhancement; Pharmacokinetic analysis; Prostate cancer

Mesh:

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Year:  2015        PMID: 25683515      PMCID: PMC4465997          DOI: 10.1016/j.mri.2015.02.008

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  41 in total

1.  Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions.

Authors:  Tobias Heye; Matthew S Davenport; Jeffrey J Horvath; Sebastian Feuerlein; Steven R Breault; Mustafa R Bashir; Elmar M Merkle; Daniel T Boll
Journal:  Radiology       Date:  2012-12-06       Impact factor: 11.105

2.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

3.  Use of patient-specific MRI-based prostate mold for validation of multiparametric MRI in localization of prostate cancer.

Authors:  Hari Trivedi; Baris Turkbey; Ardeshir R Rastinehad; Compton J Benjamin; Marcelino Bernardo; Thomas Pohida; Vijay Shah; Maria J Merino; Bradford J Wood; W Marston Linehan; Aradhana M Venkatesan; Peter L Choyke; Peter A Pinto
Journal:  Urology       Date:  2012-01       Impact factor: 2.649

Review 4.  Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker.

Authors:  Nola Hylton
Journal:  J Clin Oncol       Date:  2006-07-10       Impact factor: 44.544

5.  A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Authors:  Xia Li; E Brian Welch; Lori R Arlinghaus; A Bapsi Chakravarthy; Lei Xu; Jaime Farley; Mary E Loveless; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie A Means-Powell; Vandana G Abramson; Ana M Grau; John C Gore; Thomas E Yankeelov
Journal:  Phys Med Biol       Date:  2011-08-12       Impact factor: 3.609

6.  Role of microvessel density in predicting recurrence in pathologic Stage T3 prostatic adenocarcinoma.

Authors:  M T Gettman; A Pacelli; J Slezak; E J Bergstralh; M Blute; H Zincke; D G Bostwick
Journal:  Urology       Date:  1999-09       Impact factor: 2.649

7.  Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis.

Authors:  Susan M Galbraith; Martin A Lodge; N Jane Taylor; Gordon J S Rustin; Søren Bentzen; J James Stirling; Anwar R Padhani
Journal:  NMR Biomed       Date:  2002-04       Impact factor: 4.044

8.  Tumor angiogenesis correlates with metastasis in invasive prostate carcinoma.

Authors:  N Weidner; P R Carroll; J Flax; W Blumenfeld; J Folkman
Journal:  Am J Pathol       Date:  1993-08       Impact factor: 4.307

Review 9.  Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer.

Authors:  John V Hegde; Robert V Mulkern; Lawrence P Panych; Fiona M Fennessy; Andriy Fedorov; Stephan E Maier; Clare M C Tempany
Journal:  J Magn Reson Imaging       Date:  2013-05       Impact factor: 4.813

Review 10.  Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols.

Authors:  P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff
Journal:  J Magn Reson Imaging       Date:  1999-09       Impact factor: 4.813

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  10 in total

1.  Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate.

Authors:  Soudabeh Kargar; Eric A Borisch; Adam T Froemming; Akira Kawashima; Lance A Mynderse; Eric G Stinson; Joshua D Trzasko; Stephen J Riederer
Journal:  Magn Reson Imaging       Date:  2017-12-24       Impact factor: 2.546

2.  Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Alireza Mehrtash; Sandeep N Gupta; Dattesh Shanbhag; James V Miller; Tina Kapur; Fiona M Fennessy; Ron Kikinis; Andriy Fedorov
Journal:  J Med Imaging (Bellingham)       Date:  2016-03-01

3.  Improved performance of prostate DCE-MRI using a 32-coil vs. 12-coil receiver array.

Authors:  Stephen J Riederer; Eric A Borisch; Adam T Froemming; Roger C Grimm; Akira Kawashima; Lance A Mynderse; Joshua D Trzasko
Journal:  Magn Reson Imaging       Date:  2017-01-27       Impact factor: 2.546

4.  Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI.

Authors:  Koung Mi Kang; Seung Hong Choi; Park Chul-Kee; Tae Min Kim; Sung-Hye Park; Joo Ho Lee; Soon-Tae Lee; Inpyeong Hwang; Roh-Eul Yoo; Tae Jin Yun; Ji-Hoon Kim; Chul-Ho Sohn
Journal:  Eur Radiol       Date:  2021-05-18       Impact factor: 5.315

5.  Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues.

Authors:  Shiyang Wang; Xiaobing Fan; Milica Medved; Federico D Pineda; Ambereen Yousuf; Aytekin Oto; Gregory S Karczmar
Journal:  Magn Reson Imaging       Date:  2015-10-30       Impact factor: 2.546

6.  Investigating the role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy.

Authors:  Mehdi Taghipour; Alireza Ziaei; Francesco Alessandrino; Elmira Hassanzadeh; Mukesh Harisinghani; Mark Vangel; Clare M Tempany; Fiona M Fennessy
Journal:  Abdom Radiol (NY)       Date:  2019-04

7.  Volumetry of the dominant intraprostatic tumour lesion: intersequence and interobserver differences on multiparametric MRI.

Authors:  Hugh Harvey; Matthew R Orton; Veronica A Morgan; Chris Parker; David Dearnaley; Cyril Fisher; Nandita M deSouza
Journal:  Br J Radiol       Date:  2017-01-05       Impact factor: 3.039

8.  Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI.

Authors:  Shiyang Wang; Xiaobing Fan; Yue Zhang; Milica Medved; Dianning He; Ambereen Yousuf; Ernest Jamison; Aytekin Oto; Gregory S Karczmar
Journal:  Tomography       Date:  2019-06

9.  Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.

Authors:  Shoshana B Ginsburg; Pekka Taimen; Harri Merisaari; Paula Vainio; Peter J Boström; Hannu J Aronen; Ivan Jambor; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2016-06-10       Impact factor: 5.119

10.  A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer.

Authors:  Björn J Langbein; Filip Szczepankiewicz; Carl-Fredrik Westin; Camden Bay; Stephan E Maier; Adam S Kibel; Clare M Tempany; Fiona M Fennessy
Journal:  Invest Radiol       Date:  2021-12-01       Impact factor: 6.016

  10 in total

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