Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness
dc.contributor.author | McGarry, Sean D. | |
dc.contributor.author | Brehler, Michael | |
dc.contributor.author | Bukowy, John D. | |
dc.contributor.author | Lowman, Allison K. | |
dc.contributor.author | Bobholz, Samuel A. | |
dc.contributor.author | Duenweg, Savannah R. | |
dc.contributor.author | Banerjee, Anjishnu | |
dc.contributor.author | Hurrell, Sarah L. | |
dc.contributor.author | Malyarenko, Dariya | |
dc.contributor.author | Chenevert, Thomas L. | |
dc.contributor.author | Cao, Yue | |
dc.contributor.author | Li, Yuan | |
dc.contributor.author | You, Daekeun | |
dc.contributor.author | Fedorov, Andrey | |
dc.contributor.author | Bell, Laura C. | |
dc.contributor.author | Quarles, C. Chad | |
dc.contributor.author | Prah, Melissa A. | |
dc.contributor.author | Schmainda, Kathleen M. | |
dc.contributor.author | Taouli, Bachir | |
dc.contributor.author | LoCastro, Eve | |
dc.contributor.author | Mazaheri, Yousef | |
dc.contributor.author | Shukla-Dave, Amita | |
dc.contributor.author | Yankeelov, Thomas E. | |
dc.contributor.author | Hormuth, David A. | |
dc.contributor.author | Madhuranthakam, Ananth J. | |
dc.contributor.author | Hulsey, Keith | |
dc.contributor.author | Li, Kurt | |
dc.contributor.author | Huang, Wei | |
dc.contributor.author | Huang, Wei | |
dc.contributor.author | Muzi, Mark | |
dc.contributor.author | Jacobs, Michael A. | |
dc.contributor.author | Solaiyappan, Meiyappan | |
dc.contributor.author | Hectors, Stefanie | |
dc.contributor.author | Antic, Tatjana | |
dc.contributor.author | Paner, Gladell P. | |
dc.contributor.author | Palangmonthip, Watchareepohn | |
dc.contributor.author | Jacobsohn, Kenneth | |
dc.contributor.author | Hohenwalter, Mark | |
dc.contributor.author | Duvnjak, Petar | |
dc.contributor.author | Griffin, Michael | |
dc.contributor.author | See, William | |
dc.contributor.author | Nevalainen, Marja T. | |
dc.contributor.author | Iczkowski, Kenneth A. | |
dc.contributor.author | LaViolette, Peter S. | |
dc.date.accessioned | 2022-06-01T20:30:45Z | |
dc.date.available | 2023-07-01 16:30:40 | en |
dc.date.available | 2022-06-01T20:30:45Z | |
dc.date.issued | 2022-06 | |
dc.identifier.citation | McGarry, Sean D.; Brehler, Michael; Bukowy, John D.; Lowman, Allison K.; Bobholz, Samuel A.; Duenweg, Savannah R.; Banerjee, Anjishnu; Hurrell, Sarah L.; Malyarenko, Dariya; Chenevert, Thomas L.; Cao, Yue; Li, Yuan; You, Daekeun; Fedorov, Andrey; Bell, Laura C.; Quarles, C. Chad; Prah, Melissa A.; Schmainda, Kathleen M.; Taouli, Bachir; LoCastro, Eve; Mazaheri, Yousef; Shukla-Dave, Amita ; Yankeelov, Thomas E.; Hormuth, David A.; Madhuranthakam, Ananth J.; Hulsey, Keith; Li, Kurt; Huang, Wei; Huang, Wei; Muzi, Mark; Jacobs, Michael A.; Solaiyappan, Meiyappan; Hectors, Stefanie; Antic, Tatjana; Paner, Gladell P.; Palangmonthip, Watchareepohn; Jacobsohn, Kenneth; Hohenwalter, Mark; Duvnjak, Petar; Griffin, Michael; See, William; Nevalainen, Marja T.; Iczkowski, Kenneth A.; LaViolette, Peter S. (2022). "Multi- Site Concordance of Diffusion- Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness." Journal of Magnetic Resonance Imaging 55(6): 1745-1758. | |
dc.identifier.issn | 1053-1807 | |
dc.identifier.issn | 1522-2586 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/172844 | |
dc.publisher | John Wiley & Sons, Inc. | |
dc.subject.other | MRI | |
dc.subject.other | multisite |modelling | |
dc.subject.other | diffusion | |
dc.subject.other | cancer | |
dc.subject.other | prostate | |
dc.title | Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/172844/1/jmri27983.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/172844/2/jmri27983_am.pdf | |
dc.identifier.doi | 10.1002/jmri.27983 | |
dc.identifier.source | Journal of Magnetic Resonance Imaging | |
dc.identifier.citedreference | Stejskal EO, Tanner JE. Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965; 42 ( 1 ): 288 - 292. | |
dc.identifier.citedreference | Paudyal R, Konar AS, Obuchowski NA, et al. Repeatability of quantitative diffusion-weighted imaging metrics in phantoms, head-and-neck and thyroid cancers: Preliminary findings. Tomography 2019; 5 ( 1 ): 15 - 25. | |
dc.identifier.citedreference | Pang Y, Turkbey B, Bernardo M, et al. Intravoxel incoherent motion MR imaging for prostate cancer: An evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations. Magn Reson Med 2013; 69 ( 2 ): 553 - 562. | |
dc.identifier.citedreference | Lu Y, Jansen JF, Mazaheri Y, Stambuk HE, Koutcher JA, Shukla-Dave A. Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer. J Magn Reson Imaging 2012; 36 ( 5 ): 1088 - 1096. | |
dc.identifier.citedreference | Lewin M, Fartoux L, Vignaud A, Arrive L, Menu Y, Rosmorduc O. The diffusion-weighted imaging perfusion fraction f is a potential marker of sorafenib treatment in advanced hepatocellular carcinoma: A pilot study. Eur Radiol 2011; 21 ( 2 ): 281 - 290. | |
dc.identifier.citedreference | Langkilde F, Kobus T, Fedorov A, et al. Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging. Magn Reson Med 2018; 79 ( 4 ): 2346 - 2358. | |
dc.identifier.citedreference | Kristoffersen A. Optimal estimation of the diffusion coefficient from non-averaged and averaged noisy magnitude data. J Magn Reson 2007; 187 ( 2 ): 293 - 305. | |
dc.identifier.citedreference | Koh DM, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: Reality and challenges. AJR Am J Roentgenol 2011; 196 ( 6 ): 1351 - 1361. | |
dc.identifier.citedreference | Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010; 23 ( 7 ): 698 - 710. | |
dc.identifier.citedreference | Hectors SJ, Semaan S, Song C, et al. Advanced diffusion-weighted imaging modeling for prostate cancer characterization: Correlation with quantitative histopathologic tumor tissue composition—A hypothesis-generating study. Radiology 2018; 286 ( 3 ): 918 - 928. | |
dc.identifier.citedreference | Dyvorne HA, Galea N, Nevers T, et al. Diffusion-weighted imaging of the liver with multiple b values: Effect of diffusion gradient polarity and breathing acquisition on image quality and intravoxel incoherent motion parameters—A pilot study. Radiology 2013; 266 ( 3 ): 920 - 929. | |
dc.identifier.citedreference | McGarry SD, Bukowy JD, Iczkowski KA, et al. Gleason probability maps: A radiomics tool for mapping prostate cancer likelihood in MRI space. Tomography 2019; 5 ( 1 ): 127 - 134. | |
dc.identifier.citedreference | McGarry SD, Hurrell SL, Iczkowski KA, et al. Radio-pathomic maps of epithelium and lumen density predict the location of high-grade prostate cancer. Int J Radiat Oncol Biol Phys 2018; 101 ( 5 ): 1179 - 1187. | |
dc.identifier.citedreference | Du J, Li K, Zhang W, et al. Intravoxel incoherent motion MR imaging: Comparison of diffusion and perfusion characteristics for differential diagnosis of soft tissue tumors. Medicine (Baltimore) 2015; 94 ( 25 ): e1028. | |
dc.identifier.citedreference | Constantinides CD, Atalar E, McVeigh ER. Signal-to-noise measurements in magnitude images from NMR phased arrays. Magn Reson Med 1997; 38 ( 5 ): 852 - 857. | |
dc.identifier.citedreference | Branch MA, Coleman TF, Li Y. A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM J Sci Comput 1999; 21 ( 1 ): 1 - 23. | |
dc.identifier.citedreference | McGarry SD, Bukowy JD, Iczkowski KA, et al. Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer. J Med Imaging (Bellingham) 2020; 7 ( 5 ): 054501. | |
dc.identifier.citedreference | Hadjiiski LM, Nordstrom RJ. Quantitative imaging network: 12 years of accomplishments. Tomography 2020; 6 ( 2 ): 55. | |
dc.identifier.citedreference | Yankeelov TE, Mankoff DA, Schwartz LH, et al. Quantitative imaging in cancer clinical trials. Clin Cancer Res 2016; 22 ( 2 ): 284 - 290. | |
dc.identifier.citedreference | Farahani K, Kalpathy-Cramer J, Chenevert TL, et al. Computational challenges and collaborative projects in the NCI quantitative imaging network. Tomography 2016; 2 ( 4 ): 242 - 249. | |
dc.identifier.citedreference | Clarke LP, Nordstrom RJ, Zhang H, et al. The quantitative imaging network: NCI’s historical perspective and planned goals. Transl Oncol 2014; 7 ( 1 ): 1 - 4. | |
dc.identifier.citedreference | Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53 ( 6 ): 1432 - 1440. | |
dc.identifier.citedreference | Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168 ( 2 ): 497 - 505. | |
dc.identifier.citedreference | Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161 ( 2 ): 401 - 407. | |
dc.identifier.citedreference | Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70 ( 1 ): 7 - 30. | |
dc.identifier.citedreference | Padhani AR, Weinreb J, Rosenkrantz AB, Villeirs G, Turkbey B, Barentsz J. Prostate Imaging-Reporting and Data System Steering Committee: PI-RADS v2 status update and future directions. Eur Urol 2019; 75 ( 3 ): 385 - 396. | |
dc.identifier.citedreference | Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 2018; 378 ( 19 ): 1767 - 1777. | |
dc.identifier.citedreference | Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 2019; 76 ( 3 ): 340 - 351. | |
dc.identifier.citedreference | Vargas HA, Hotker AM, Goldman DA, et al. Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: Critical evaluation using whole-mount pathology as standard of reference. Eur Radiol 2016; 26 ( 6 ): 1606 - 1612. | |
dc.identifier.citedreference | Hurrell SL, McGarry SD, Kaczmarowski A, et al. Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging. J Med Imaging (Bellingham) 2018; 5 ( 1 ): 011004. | |
dc.identifier.citedreference | Newitt DC, Malyarenko D, Chenevert TL, et al. Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 2018; 5 ( 1 ): 011003. | |
dc.identifier.citedreference | Nir G, Hor S, Karimi D, et al. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts. Med Image Anal 2018; 50: 167 - 180. | |
dc.identifier.citedreference | Nir G, Karimi D, Goldenberg SL, et al. Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images. JAMA Netw Open 2019; 2 ( 3 ): e190442. | |
dc.identifier.citedreference | Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; 1967. | |
dc.identifier.citedreference | Malyarenko D, Pang Y, Amouzandeh G, Chenevert T. Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion. Vol 11313: SPIE; 2020. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/113130W/Numerical-DWI-phantoms-to-optimize-accuracy-and-precision-of-quantitative/10.1117/12.2549412.short?SSO=1 | |
dc.working.doi | NO | en |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.