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

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, 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, and Peter S LaViolette. 2022. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging, 55, 6, Pp. 1745-58.
Copy at


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.
Last updated on 02/24/2023