Multi-site concordance of diffusion weighted imaging quantification for assessing prostate cancer aggressiveness

Details

• Alternative Title:
  J Magn Reson Imaging
• Personal Author:
  McGarry, Sean D. ; Brehler, Michael ; Bukowy, John D. ; Lowman, Allison K ; Bobholz, Samuel ; Duenweg, Savannah ; 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 ; Palangmonthip, Watchareepohn ; Jacobsohn, Kenneth ; Hohenwalter, Mark ; Duvnjak, Petar ; Griffin, Michael ; See, William ; Nevalainen, Marja ; Iczkowski, Kenneth A. ; LaViolette, Peter S.
• Description:
  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:
  
  33 patients prospectively imaged prior to prostatectomy.
  
  Field Strength/Sequence:
  
  3T, field-of-view optimized and constrained undistorted single-shot (FOCUS) 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 monoexponential 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.
  
  
• Subjects:
  ADC Mapping Article Cancer Diffusion Diffusion Magnetic Resonance Imaging Humans Male MRI Multiparametric Multisite Prospective Studies Prostate Prostatic Neoplasms Retrospective Studies ROC Curve Sensitivity And Specificity T2-weighted

  More +
• Source:
  J Magn Reson Imaging. 55(6):1745-1758
• Pubmed ID:
  34767682
• Pubmed Central ID:
  PMC9095769
• Document Type:
  Journal Article
• Funding:
  U24 CA180918/CA/NCI NIH HHSUnited States/ ; R01 CA158079/CA/NCI NIH HHSUnited States/ ; R01 CA241817/CA/NCI NIH HHSUnited States/ ; TL1 TR001437/TR/NCATS NIH HHSUnited States/ ; U01 CA183848/CA/NCI NIH HHSUnited States/ ; R50 CA211270/CA/NCI NIH HHSUnited States/ ; R01 CA228036/CA/NCI NIH HHSUnited States/ ; U01 CA151261/CA/NCI NIH HHSUnited States/ ; U01 CE002944/CE/NCIPC CDC HHSUnited States/ ; R01 CA160902/CA/NCI NIH HHSUnited States/ ; R21 CA231892/CA/NCI NIH HHSUnited States/ ; R01 CA218144/CA/NCI NIH HHSUnited States/ ; P01 CA085878/CA/NCI NIH HHSUnited States/ ; R01 CA249882/CA/NCI NIH HHSUnited States/ ; R01 CA190299/CA/NCI NIH HHSUnited States/ ; U01 CA142565/CA/NCI NIH HHSUnited States/ ; P30 CA006973/CA/NCI NIH HHSUnited States/ ; UG3 CA247606/CA/NCI NIH HHSUnited States/ ; U01 CA140204/CA/NCI NIH HHSUnited States/ ; P41 EB015898/EB/NIBIB NIH HHSUnited States/ ; P30 CA008748/CA/NCI NIH HHSUnited States/ ; U01 CA166104/CA/NCI NIH HHSUnited States/ ; U01 CA172320/CA/NCI NIH HHSUnited States/ ; U01 CA154602/CA/NCI NIH HHSUnited States/ ; UL1 TR001436/TR/NCATS NIH HHSUnited States/ ; U01 NS088034/NS/NINDS NIH HHSUnited States/ ; R01 CA221938/CA/NCI NIH HHSUnited States/ ; R01 CA248192/CA/NCI NIH HHSUnited States/ ; U01 CA176110/CA/NCI NIH HHSUnited States/ ; P41 EB028741/EB/NIBIB NIH HHSUnited States/ ; U01 CA207091/CA/NCI NIH HHSUnited States/ ; U01 CA211205/CA/NCI NIH HHSUnited States/
• Volume:
  55
• Issue:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:4530b90857c1d578bb1a64ddb3f5d29ad1d17aaa99a97227dfe99b933ac72b75
• Download URL:
  https://stacks.cdc.gov/view/cdc/119180/cdc_119180_DS1.pdf
• File Type:
  [PDF - 2.75 MB ]