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

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.

Keywords: MRI; cancer; diffusion; multisite |modelling; prostate.

FIGURE 1

Schematic representation of the experimental design. Top: Raw diffusion data distributed to partner institutions in DICOM format, partner institutions return fits to MCW where they were manually aligned to the T2‐weighted image. Bottom: Post‐surgery, tissue was sliced to match the T2‐weighted image using patient‐specific slicing jigs. Whole‐mount samples were stained and annotated by a pathologist. Annotations were then aligned to the T2‐weighted image., , , Right: Pathologist annotations and fits from multiple institutions were combined for analysis to determine variability in prostate cancer sensitivity and specificity.

FIGURE 2

A summary of submitted site diffusion‐weighted imaging (DWI) fit parameter maps aligned to a pathologist‐annotated whole‐mount histology slide. Left: The corresponding T2‐weighted slice, pathologist annotations in histology space containing a dominant G4 fused gland (G4FG) tumor (yellow) with a secondary G3 region (green) and two small G4 cribriform gland (G4CG) tumors (Pink). Left bottom shows the pathologist annotations aligned in MRI space and overlaid on the T2. Right: Site implementations included mono‐exponential apparent diffusion coefficient (MEADC), bi‐exponential diffusion (BID), pseudo‐diffusion (BID* [×10⁻³ mm²/second]), and perfusion fraction (BID [×10⁻³ mm²/second], BID* [×10⁻³ mm²/second], and F), and kurtosis and kurtosis diffusion (K and DK). Some sites submitted multiple sets of fits, each implementation is separated and treated separately. Relative contrast differences between sites are notable in the MEADC images, but independent of implementation the tumor has decreased diffusion compared to surrounding tissue. Bi‐exponential fits showed notable contrast differences between site implementation while kurtosis fits were notably similar.

FIGURE 3

Percent difference matrix comparing DWI parameters between site implementation (SI) and between classes of cancer and normal (Top). Pearson correlation coefficient matrices comparing DWI parameters between SI and classes of cancer and non‐cancer (Bottom). MEADC, K, and DK show the least percent difference across sites and the highest correlation. Data are shown in Tables S2 and S3 in the Supplemental Material. DWI = diffusion‐weighted imaging; MEADC = mono‐exponential apparent diffusion coefficient; K = kurtosis; DK = diffusion kurtosis.

FIGURE 4

Boxplot showing area under the curve receiver operating characteristic (ROC AUC) variability by site implemented fits. Left: Cancer (G3+) vs. benign atrophy. Right: Gleason 3 vs. Gleason 4+. ROC AUC was calculated lesion‐wise using the median value in each pathologist annotated region larger than 200 voxels. A tighter boxplot indicates less cancer differentiation variability between site implementations.

FIGURE 5

Receiver operator characteristic area under the curve (ROC AUC) for all institutions grouped by fit and repeated varying the minimum lesion size included in the analysis. Lesion size limit was varied from 100 voxels to 500 voxels stratifying G3+ vs. benign atrophy (Left) and stratifying G3 from high‐grade tumors (Right). There is a trend towards increasing AUC as the cluster limit for inclusion becomes more selective in both cancer vs. benign and low‐grade vs. high‐grade. Fits that are highly variable between sites remain highly variable independent of cluster limit.

FIGURE 6

Area under the curve receiver operator characteristic (ROC AUC) for cancer vs. regions left unlabeled by all pathologists (unlabeled consensus) annotations varying the pathologist annotating the slides. Left: Sample annotations from all five observers on a representative whole‐mount prostate slide. Right: Boxplots showing AUCs varying the image contrast and observer annotating the slides. Median values were extracted from regions of interest (ROIs) greater than 200 voxels in plane.