Abstract #0583

Rad-Path correlation and machine learning generate epithelium density maps predictive of pathologically confirmed prostate cancer

Amy L. Kaczmarowski¹, Kenneth Iczkowski², William A. Hall³, Ahmad M. El-Arabi⁴, Kenneth Jacobsohn⁴, Paul Knechtges¹, Mark Hohenwalter¹, William See⁴, and Peter S. LaViolette¹

¹Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, ²Pathology, Medical College of Wisconsin, Milwaukee, WI, United States, ³Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States, ⁴Urology, Medical College of Wisconsin, Milwaukee, WI, United States

Radiological-pathological correlation is being used to validate prostate cancer imaging technology. This study combines these two modalities with machine learning to generate predictive maps of histological features (i.e. new contrasts) based on segmented histology. We find that epithelium density maps highlight regions pathologically confirmed as Gleason grade ≥3. This allowed the prediction of prostate cancer presence based solely on non-invasive imaging in 23 of 26 cases.

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