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Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness
Author(s) -
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,
ShuklaDave 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.
Publication year - 2022
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27983
Subject(s) - prostate cancer , concordance , diffusion mri , medicine , nuclear medicine , magnetic resonance imaging , radiology , cancer
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