In Journal of medical imaging (Bellingham, Wash.)
Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.
McGarry Sean D, Bukowy John D, Iczkowski Kenneth A, Lowman Allison K, Brehler Michael, Bobholz Samuel, Nencka Andrew, Barrington Alex, Jacobsohn Kenneth, Unteriner Jackson, Duvnjak Petar, Griffin Michael, Hohenwalter Mark, Keuter Tucker, Huang Wei, Antic Tatjana, Paner Gladell, Palangmonthip Watchareepohn, Banerjee Anjishnu, LaViolette Peter S
machine learning, magnetic resonance imaging, prostate cancer, rad-path