Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial.


Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
06 2019
Historique:
received: 07 03 2018
revised: 20 09 2018
accepted: 22 09 2018
pubmed: 24 10 2018
medline: 7 10 2020
entrez: 24 10 2018
Statut: ppublish

Résumé

Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker. To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures. Prospective. In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer. DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T. A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient. Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients. In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10 Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

Sections du résumé

BACKGROUND
Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker.
PURPOSE
To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures.
STUDY TYPE
Prospective.
SUBJECTS
In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer.
FIELD STRENGTH/SEQUENCE
DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T.
ASSESSMENT
A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient.
STATISTICAL TESTS
Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients.
RESULTS
In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10
DATA CONCLUSION
Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

Identifiants

pubmed: 30350329
doi: 10.1002/jmri.26539
pmc: PMC6524146
mid: NIHMS1027800
doi:

Substances chimiques

Biomarkers 0
Contrast Media 0
ERBB2 protein, human EC 2.7.10.1
Receptor, ErbB-2 EC 2.7.10.1

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1617-1628

Subventions

Organisme : NCI NIH HHS
ID : U01 CA225427
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA190299
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015894
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA080098
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA140204
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA166104
Pays : United States
Organisme : NCI NIH HHS
ID : UG1 CA189828
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA079778
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA151235
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180794
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA132870
Pays : United States
Organisme : NCI NIH HHS
ID : UG1 CA233160
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180820
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA151326
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA180803
Pays : United States

Informations de copyright

© 2018 International Society for Magnetic Resonance in Medicine.

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Auteurs

David C Newitt (DC)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Zheng Zhang (Z)

Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA.
American College of Radiology Imaging Network (ACRIN), Philadelphia, Pennsylvania, USA.

Jessica E Gibbs (JE)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Savannah C Partridge (SC)

Department of Radiology, University of Washington, Seattle, Washington, USA.

Thomas L Chenevert (TL)

Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.

Mark A Rosen (MA)

Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Patrick J Bolan (PJ)

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA.

Helga S Marques (HS)

Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA.
American College of Radiology Imaging Network (ACRIN), Philadelphia, Pennsylvania, USA.

Sheye Aliu (S)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Wen Li (W)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Lisa Cimino (L)

American College of Radiology & ECOG-ACRIN Cancer Research Group, Philadelphia, Pennsylvania, USA.

Bonnie N Joe (BN)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Heidi Umphrey (H)

Department of Radiology, University of Alabama, Birmingham, Alabama, USA.

Haydee Ojeda-Fournier (H)

Department of Radiology, University of California, San Diego, California, USA.

Basak Dogan (B)

Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Houston, Texas, USA.

Karen Oh (K)

Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.

Hiroyuki Abe (H)

Department of Radiology, University of Chicago, Chicago, Illinois, USA.

Jennifer Drukteinis (J)

H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA.
Department of Women's Imaging, St. Joseph's Women's Hospital, Tampa, Florida, USA.

Laura J Esserman (LJ)

Department of Surgery, University of California, San Francisco, California, USA.

Nola M Hylton (NM)

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

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