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McGarry SD, Brehler M, Bukowy JD, et al. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging. 2021;doi: 10.1002/jmri.27983.
McGarry, S. D., Brehler, M., Bukowy, J. D., Lowman, A. K., Bobholz, S. A., Duenweg, S. R., Banerjee, A., Hurrell, S. L., Malyarenko, D., Chenevert, T. L., Cao, Y., Li, Y., You, D., Fedorov, A., Bell, L. C., Quarles, C. C., Prah, M. A., Schmainda, K. M., Taouli, B., LoCastro, E., Mazaheri, Y., Shukla-Dave, A., Yankeelov, T. E., Hormuth, D. A., Madhuranthakam, A. J., Hulsey, K., Li, K., Huang, W., Huang, W., Muzi, M., Jacobs, M. A., Solaiyappan, M., Hectors, S., Antic, T., Paner, G. P., Palangmonthip, W., Jacobsohn, K., Hohenwalter, M., Duvnjak, P., Griffin, M., See, W., Nevalainen, M. T., Iczkowski, K. A., & LaViolette, P. S. (2021). Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.27983
McGarry, Sean D, et al. "Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness." Journal of magnetic resonance imaging : JMRI vol. (2021). doi: https://doi.org/10.1002/jmri.27983
McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y, Li Y, You D, Fedorov A, Bell LC, Quarles CC, Prah MA, Schmainda KM, Taouli B, LoCastro E, Mazaheri Y, Shukla-Dave A, Yankeelov TE, Hormuth DA, Madhuranthakam AJ, Hulsey K, Li K, Huang W, Huang W, Muzi M, Jacobs MA, Solaiyappan M, Hectors S, Antic T, Paner GP, Palangmonthip W, Jacobsohn K, Hohenwalter M, Duvnjak P, Griffin M, See W, Nevalainen MT, Iczkowski KA, LaViolette PS. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging. 2021 Nov 12; doi: 10.1002/jmri.27983. Epub 2021 Nov 12. PMID: 34767682.
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J Magn Reson Imaging. 2021 Nov 12; doi: 10.1002/jmri.27983. Epub 2021 Nov 12.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

Journal of magnetic resonance imaging : JMRI

Sean D McGarry, Michael Brehler, John D Bukowy, Allison K Lowman, Samuel A Bobholz, Savannah R Duenweg, Anjishnu Banerjee, Sarah L Hurrell, Dariya Malyarenko, Thomas L Chenevert, Yue Cao, Yuan Li, Daekeun You, Andrey Fedorov, Laura C Bell, C Chad Quarles, Melissa A Prah, Kathleen M Schmainda, Bachir Taouli, Eve LoCastro, Yousef Mazaheri, Amita Shukla-Dave, Thomas E Yankeelov, David A Hormuth, Ananth J Madhuranthakam, Keith Hulsey, Kurt Li, Wei Huang, Wei Huang, Mark Muzi, Michael A Jacobs, Meiyappan Solaiyappan, Stefanie Hectors, Tatjana Antic, Gladell P Paner, Watchareepohn Palangmonthip, Kenneth Jacobsohn, Mark Hohenwalter, Petar Duvnjak, Michael Griffin, William See, Marja T Nevalainen, Kenneth A Iczkowski, Peter S LaViolette

Affiliations

  1. Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  2. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
  3. Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, WI, USA.
  4. Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  5. Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
  6. Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  7. Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  8. Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA.
  9. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  10. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  11. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  12. Department of Biomedical Engineering, Diagnostic Medicine, Oncology, Oden Institute for Computational Engineering and Sciences, Livestrong Cancer Institutes, The University of Texas, Austin, Texas, USA.
  13. Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  14. International School of Beaverton, Aloha, Oregon, USA.
  15. Advanced Imaging Research Center, Oregon Health Sciences University, Portland, Oregon, USA.
  16. Department of Pathology, Oregon Health and Science University, Madison, Wisconsin, USA.
  17. Department of Radiology, Neurology, and Radiation Oncology, University of Washington, Seattle, Washington, USA.
  18. The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  19. Department of biomedical engineering and imaging institute, Weill Cornell Medical College, New York City, New York, USA.
  20. Department of Pathology, University of Chicago, Chicago, Illinois, USA.
  21. Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  22. Department of Pathology, Chiang Mai University, Chiang Mai, Thailand.
  23. Department of Urology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  24. Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

PMID: 34767682 DOI: 10.1002/jmri.27983

Abstract

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.

PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.

STUDY TYPE: Prospective.

POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy.

FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence.

ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).

STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.

RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.

DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer.

LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Keywords: MRI; cancer; diffusion; multisite |modelling; prostate

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