Extracranial Soft-Tissue Tumors: Repeatability of Apparent Diffusion Coefficient Estimates from Diffusion-weighted MR Imaging.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

[1]  Barry N. Taylor,et al.  Guidelines for Evaluating and Expressing the Uncertainty of Nist Measurement Results , 2017 .

[2]  B. Taouli,et al.  Intravoxel incoherent motion diffusion‐weighted imaging of hepatocellular carcinoma: Is there a correlation with flow and perfusion metrics obtained with dynamic contrast‐enhanced MRI? , 2016, Journal of magnetic resonance imaging : JMRI.

[3]  C. Lehman,et al.  Diffusion‐weighted imaging outside the brain: Consensus statement from an ISMRM‐sponsored workshop , 2016, Journal of magnetic resonance imaging : JMRI.

[4]  D. Collins,et al.  Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort , 2016, European Radiology.

[5]  Andrew N Priest,et al.  A framework for optimization of diffusion-weighted MRI protocols for large field-of-view abdominal-pelvic imaging in multicenter studies. , 2015, Medical physics.

[6]  Erich P Huang,et al.  Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.

[7]  Martin O Leach,et al.  Diffusion-weighted MR imaging of metastatic abdominal and pelvic tumours is sensitive to early changes induced by a VEGF inhibitor using alternative diffusion attenuation models , 2015, European Radiology.

[8]  J. Deasy,et al.  Repeatability Investigation of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging on Thyroid Glands , 2015, Journal of computer assisted tomography.

[9]  D. Collins,et al.  Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study , 2015, European Radiology.

[10]  O. Craciunescu,et al.  Diffusion-weighted imaging for head and neck squamous cell carcinoma: quantifying repeatability to understand early treatment-induced change. , 2014, AJR. American journal of roentgenology.

[11]  Martin O. Leach,et al.  Interrogating Two Schedules of the AKT Inhibitor MK-2206 in Patients with Advanced Solid Tumors Incorporating Novel Pharmacodynamic and Functional Imaging Biomarkers , 2014, Clinical Cancer Research.

[12]  Namkug Kim,et al.  Reproducibility of measurement of apparent diffusion coefficients of malignant hepatic tumors: Effect of DWI techniques and calculation methods , 2012, Journal of magnetic resonance imaging : JMRI.

[13]  D. Collins,et al.  Advanced solid tumors treated with cediranib: comparison of dynamic contrast-enhanced MR imaging and CT as markers of vascular activity. , 2012, Radiology.

[14]  R. Boubertakh,et al.  In vitro and in vivo repeatability of abdominal diffusion-weighted MRI. , 2012, The British journal of radiology.

[15]  H. Honda,et al.  Non-small cell lung cancer: detection of early response to chemotherapy by using contrast-enhanced dynamic and diffusion-weighted MR imaging. , 2011, Radiology.

[16]  D. Collins,et al.  Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. , 2011, Radiology.

[17]  D. Collins,et al.  Phase I trial of a selective c-MET inhibitor ARQ 197 incorporating proof of mechanism pharmacodynamic studies. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  Huiman X Barnhart,et al.  Applications of the repeatability of quantitative imaging biomarkers: a review of statistical analysis of repeat data sets. , 2009, Translational oncology.

[19]  Martin O. Leach,et al.  Reproducibility and changes in the apparent diffusion coefficients of solid tumours treated with combretastatin A4 phosphate and bevacizumab in a two-centre phase I clinical trial , 2009, European Radiology.

[20]  G. Weinstein,et al.  Diffusion-Weighted Magnetic Resonance Imaging for Predicting and Detecting Early Response to Chemoradiation Therapy of Squamous Cell Carcinomas of the Head and Neck , 2009, Clinical Cancer Research.

[21]  U. Sharma,et al.  Longitudinal study of the assessment by MRI and diffusion‐weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2009, NMR in biomedicine.

[22]  F. Gilbert,et al.  Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. , 2008, Gynecologic oncology.

[23]  L. Shen,et al.  Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. , 2008, Radiology.

[24]  D. Collins,et al.  Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. , 2007, AJR. American journal of roentgenology.

[25]  Jan Wolber,et al.  Diffusion MRI for prediction of response of rectal cancer to chemoradiation , 2002, The Lancet.

[26]  S. O. Oyadiji,et al.  Application of coefficient of variation in reliability-based mechanical design and manufacture , 2001 .

[27]  W. Stahel,et al.  Log-normal Distributions across the Sciences: Keys and Clues , 2001 .

[28]  O. Keene,et al.  The log transformation is special. , 1995, Statistics in medicine.

[29]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[30]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[31]  Epsrc Cancer.  Metastatic ovarian and primary peritoneal cancer:assessing chemotherapy response with diffusion-weighted MR imaging—value of histogram analysis of apparent diffusion coefficients , 2011 .

[32]  Mithat Gönen,et al.  Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment , 2015, Statistical methods in medical research.