Faculty Collaboration Database

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging 2022 Jun;55(6):1745-1758

Date

11/13/2021

Pubmed ID

34767682

Pubmed Central ID

PMC9095769

DOI

10.1002/jmri.27983

Scopus ID

2-s2.0-85118887092 (requires institutional sign-in at Scopus site)   11 Citations

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.

Author List

McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y, Li Y, You D, Fedorov A, Bell LC, Quarles CC, Prah MA, Schmainda KM, Taouli B, LoCastro E, Mazaheri Y, Shukla-Dave A, Yankeelov TE, Hormuth DA 2nd, Madhuranthakam AJ, Hulsey K, Li K, Huang W, Huang W, Muzi M, Jacobs MA, Solaiyappan M, Hectors S, Antic T, Paner GP, Palangmonthip W, Jacobsohn K, Hohenwalter M, Duvnjak P, Griffin M, See W, Nevalainen MT, Iczkowski KA, LaViolette PS

Authors

Anjishnu Banerjee PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Samuel Bobholz PhD Assistant Professor in the Radiology department at Medical College of Wisconsin
Savannah R. Duenweg Research Scientist I in the Radiology department at Medical College of Wisconsin
Michael O. Griffin PhD, MD Associate Professor in the Radiology department at Medical College of Wisconsin
Mark D. Hohenwalter MD Associate Dean, Executive Director, Professor in the Radiology department at Medical College of Wisconsin
Peter LaViolette PhD Vice Chair, Professor in the Radiology department at Medical College of Wisconsin
Kathleen M. Schmainda PhD Professor in the Biophysics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Diffusion Magnetic Resonance Imaging
Humans
Male
Prospective Studies
Prostatic Neoplasms
ROC Curve
Retrospective Studies
Sensitivity and Specificity