Literature DB >> 26066626

Repeatability of Standardized and Normalized Relative CBV in Patients with Newly Diagnosed Glioblastoma.

M A Prah1, S M Stufflebeam2, E S Paulson3, J Kalpathy-Cramer2, E R Gerstner2, T T Batchelor2, D P Barboriak4, B R Rosen2, K M Schmainda5.   

Abstract

BACKGROUND AND
PURPOSE: For more widespread clinical use advanced imaging methods such as relative cerebral blood volume must be both accurate and repeatable. The aim of this study was to determine the repeatability of relative CBV measurements in newly diagnosed glioblastoma multiforme by using several of the most commonly published estimation techniques.
MATERIALS AND METHODS: The relative CBV estimates were calculated from dynamic susceptibility contrast MR imaging in double-baseline examinations for 33 patients with treatment-naïve and pathologically proved glioblastoma multiforme (men = 20; mean age = 55 years). Normalized and standardized relative CBV were calculated by using 6 common postprocessing methods. The repeatability of both normalized and standardized relative CBV, in both tumor and contralateral brain, was examined for each method with metrics of repeatability, including the repeatability coefficient and within-subject coefficient of variation. The minimum sample size required to detect a parameter change of 10% or 20% was also determined for both normalized relative CBV and standardized relative CBV for each estimation method.
RESULTS: When ordered by the repeatability coefficient, methods using postprocessing leakage correction and ΔR2*(t) techniques offered superior repeatability. Across processing techniques, the standardized relative CBV repeatability in normal-appearing brain was comparable with that in tumor (P = .31), yet inferior in tumor for normalized relative CBV (P = .03). On the basis of the within-subject coefficient of variation, tumor standardized relative CBV estimates were less variable (13%-20%) than normalized relative CBV estimates (24%-67%). The minimum number of participants needed to detect a change of 10% or 20% is 118-643 or 30-161 for normalized relative CBV and 109-215 or 28-54 for standardized relative CBV.
CONCLUSIONS: The ΔR2* estimation methods that incorporate leakage correction offer the best repeatability for relative CBV, with standardized relative CBV being less variable and requiring fewer participants to detect a change compared with normalized relative CBV.
© 2015 by American Journal of Neuroradiology.

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Mesh:

Year:  2015        PMID: 26066626      PMCID: PMC4567906          DOI: 10.3174/ajnr.A4374

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  34 in total

1.  Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements.

Authors:  L S Hu; L C Baxter; K A Smith; B G Feuerstein; J P Karis; J M Eschbacher; S W Coons; P Nakaji; R F Yeh; J Debbins; J E Heiserman
Journal:  AJNR Am J Neuroradiol       Date:  2008-12-04       Impact factor: 3.825

Review 2.  The role of clinical imaging in oncological drug development.

Authors:  P S Murphy; T J McCarthy; A S K Dzik-Jurasz
Journal:  Br J Radiol       Date:  2008-06-09       Impact factor: 3.039

Review 3.  Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma.

Authors:  L C Hygino da Cruz; I Rodriguez; R C Domingues; E L Gasparetto; A G Sorensen
Journal:  AJNR Am J Neuroradiol       Date:  2011-03-10       Impact factor: 3.825

4.  Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas.

Authors:  H Thomsen; E Steffensen; E-M Larsson
Journal:  Acta Radiol       Date:  2011-11-23       Impact factor: 1.990

5.  Role of perfusion-weighted imaging at 3T in the histopathological differentiation between astrocytic and oligodendroglial tumors.

Authors:  Taiichi Saito; Fumiyuki Yamasaki; Yoshinori Kajiwara; Nobukazu Abe; Yuji Akiyama; Takako Kakuda; Yukio Takeshima; Kazuhiko Sugiyama; Yoshikazu Okada; Kaoru Kurisu
Journal:  Eur J Radiol       Date:  2011-05-04       Impact factor: 3.528

6.  Standardization of relative cerebral blood volume (rCBV) image maps for ease of both inter- and intrapatient comparisons.

Authors:  Devyani Bedekar; Todd Jensen; Kathleen M Schmainda
Journal:  Magn Reson Med       Date:  2010-09       Impact factor: 4.668

7.  Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma.

Authors:  Kathleen M Schmainda; Melissa Prah; Jennifer Connelly; Scott D Rand; Raymond G Hoffman; Wade Mueller; Mark G Malkin
Journal:  Neuro Oncol       Date:  2014-01-15       Impact factor: 12.300

8.  Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation.

Authors:  Tracy T Batchelor; Elizabeth R Gerstner; Kyrre E Emblem; Dan G Duda; Jayashree Kalpathy-Cramer; Matija Snuderl; Marek Ancukiewicz; Pavlina Polaskova; Marco C Pinho; Dominique Jennings; Scott R Plotkin; Andrew S Chi; April F Eichler; Jorg Dietrich; Fred H Hochberg; Christine Lu-Emerson; A John Iafrate; S Percy Ivy; Bruce R Rosen; Jay S Loeffler; Patrick Y Wen; A Greg Sorensen; Rakesh K Jain
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-04       Impact factor: 11.205

9.  Utility of multiparametric 3-T MRI for glioma characterization.

Authors:  Bhaswati Roy; Rakesh K Gupta; Andrew A Maudsley; Rishi Awasthi; Sulaiman Sheriff; Meng Gu; Nuzhat Husain; Sudipta Mohakud; Sanjay Behari; Chandra M Pandey; Ram K S Rathore; Daniel M Spielman; Jeffry R Alger
Journal:  Neuroradiology       Date:  2013-02-02       Impact factor: 2.804

10.  Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting.

Authors:  Gisele B Caseiras; Sophie Chheang; James Babb; Jeremy H Rees; Nicole Pecerrelli; Daniel J Tozer; Christopher Benton; David Zagzag; Glyn Johnson; Adam D Waldman; H R Jäger; Meng Law
Journal:  Eur J Radiol       Date:  2009-02-06       Impact factor: 3.528

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  21 in total

1.  Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies.

Authors:  J M Hoxworth; J M Eschbacher; A C Gonzales; K W Singleton; G D Leon; K A Smith; A M Stokes; Y Zhou; G L Mazza; A B Porter; M M Mrugala; R S Zimmerman; B R Bendok; D P Patra; C Krishna; J L Boxerman; L C Baxter; K R Swanson; C C Quarles; K M Schmainda; L S Hu
Journal:  AJNR Am J Neuroradiol       Date:  2020-03-12       Impact factor: 3.825

2.  Analyzing magnetic resonance imaging data from glioma patients using deep learning.

Authors:  Bjoern Menze; Fabian Isensee; Roland Wiest; Bene Wiestler; Klaus Maier-Hein; Mauricio Reyes; Spyridon Bakas
Journal:  Comput Med Imaging Graph       Date:  2020-12-02       Impact factor: 4.790

3.  Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low-Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors.

Authors:  K M Schmainda; M A Prah; L S Hu; C C Quarles; N Semmineh; S D Rand; J M Connelly; B Anderies; Y Zhou; Y Liu; B Logan; A Stokes; G Baird; J L Boxerman
Journal:  AJNR Am J Neuroradiol       Date:  2019-03-28       Impact factor: 3.825

Review 4.  Translating preclinical MRI methods to clinical oncology.

Authors:  David A Hormuth; Anna G Sorace; John Virostko; Richard G Abramson; Zaver M Bhujwalla; Pedro Enriquez-Navas; Robert Gillies; John D Hazle; Ralph P Mason; C Chad Quarles; Jared A Weis; Jennifer G Whisenant; Junzhong Xu; Thomas E Yankeelov
Journal:  J Magn Reson Imaging       Date:  2019-03-29       Impact factor: 4.813

5.  Quantitative Delta T1 (dT1) as a Replacement for Adjudicated Central Reader Analysis of Contrast-Enhancing Tumor Burden: A Subanalysis of the American College of Radiology Imaging Network 6677/Radiation Therapy Oncology Group 0625 Multicenter Brain Tumor Trial.

Authors:  K M Schmainda; M A Prah; Z Zhang; B S Snyder; S D Rand; T R Jensen; D P Barboriak; J L Boxerman
Journal:  AJNR Am J Neuroradiol       Date:  2019-06-27       Impact factor: 3.825

6.  The Initial Area Under the Curve Derived from Dynamic Contrast-Enhanced MRI Improves Prognosis Prediction in Glioblastoma with Unmethylated MGMT Promoter.

Authors:  Y S Choi; S S Ahn; H-J Lee; J H Chang; S-G Kang; E H Kim; S H Kim; S-K Lee
Journal:  AJNR Am J Neuroradiol       Date:  2017-06-22       Impact factor: 3.825

7.  Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients.

Authors:  Ashley M Stokes; Maurizio Bergamino; Lea Alhilali; Leland S Hu; John P Karis; Leslie C Baxter; Laura C Bell; C Chad Quarles
Journal:  J Cereb Blood Flow Metab       Date:  2021-08-20       Impact factor: 6.960

8.  Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics.

Authors:  Melissa A Prah; Mona M Al-Gizawiy; Wade M Mueller; Elizabeth J Cochran; Raymond G Hoffmann; Jennifer M Connelly; Kathleen M Schmainda
Journal:  J Neurooncol       Date:  2017-09-12       Impact factor: 4.130

9.  Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas.

Authors:  Jerrold L Boxerman; Chad C Quarles; Leland S Hu; Bradley J Erickson; Elizabeth R Gerstner; Marion Smits; Timothy J Kaufmann; Daniel P Barboriak; Raymond H Huang; Wolfgang Wick; Michael Weller; Evanthia Galanis; Jayashree Kalpathy-Cramer; Lalitha Shankar; Paula Jacobs; Caroline Chung; Martin J van den Bent; Susan Chang; W K Al Yung; Timothy F Cloughesy; Patrick Y Wen; Mark R Gilbert; Bruce R Rosen; Benjamin M Ellingson; Kathleen M Schmainda
Journal:  Neuro Oncol       Date:  2020-09-29       Impact factor: 12.300

Review 10.  The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.

Authors:  Robert H Press; Hui-Kuo G Shu; Hyunsuk Shim; James M Mountz; Brenda F Kurland; Richard L Wahl; Ella F Jones; Nola M Hylton; Elizabeth R Gerstner; Robert J Nordstrom; Lori Henderson; Karen A Kurdziel; Bhadrasain Vikram; Michael A Jacobs; Matthias Holdhoff; Edward Taylor; David A Jaffray; Lawrence H Schwartz; David A Mankoff; Paul E Kinahan; Hannah M Linden; Philippe Lambin; Thomas J Dilling; Daniel L Rubin; Lubomir Hadjiiski; John M Buatti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-06-30       Impact factor: 7.038

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