Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. Issue 6 (12th November 2021)

• Record Type: Journal Article
• Authors: 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.;
• Is Part Of: Journal of magnetic resonance imaging. Volume 55:Issue 6(2022)
• Page Start: 1745
• Page End: 1758
• Publication Date: 2021-11-12
• Subjects: diffusion -- MRI -- prostate -- cancer -- multisite |modelling; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27983
• Languages: English
• ISSNs: 1053-1807

Abstract

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