Abstract #0874

Investigatory usage of a framework for automated cancer annotation of pathology slides of radical prostatectomy specimens: effect on performance of a predictive model for mpMRI detection of prostate cancer.

Ethan Leng¹, Jin Jin², Jonathan C Henriksen³, Joseph S Koopmeiners², Stephen C Schmechel³, and Gregory J Metzger¹

¹Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, ²Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States, ³Department of Pathology, University of Washington, Seattle, WA, United States

The development of CAD systems for prostate cancer detection requires large amounts of training data with correlated pathologic ground truth. The gold standard is manual annotation of cancer by pathologists, which is tedious and difficult to obtain. Here, we retrospectively applied a previously-described digital-pathology framework for automating cancer annotation. We trained a Bayesian predictive model on the original ground truth (from manual annotation) and on the new ground truth, and compared the performances. The results suggest the ground truths are very similar and largely equivalent, which provides support for prospective usage of our approach for automatic annotation of prostate cancer.

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