Abstract #1515

Multi-parametric versus bi-parametric prostate MRI for deep learning: Marginal benefits from adding dynamic contrast-enhanced images

Nils Bastian Netzer^1,2, Adrian Schrader^1,2, Magdalena Görtz³, Constantin Schwab⁴, Markus Hohenfellner³, Heinz-Peter Schlemmer¹, and David Bonekamp¹

¹Radiology, German Cancer Research Center, Heidelberg, Germany, ²Heidelberg University Medical School, Heidelberg, Germany, ³Urology, University of Heidelberg Medical Center, Heidelberg, Germany, ⁴Pathology, University of Heidelberg Medical Center, Heidelberg, Germany

Synopsis

The value of dynamic contrast enhanced MRI (DCE) for the diagnosis of prostate cancer is unclear and has not yet been investigated in the context of deep learning. We trained 3D U-Nets to segment prostate cancer on bi-parametric MRI and on DCE images of 761 exams. On a test set of 191 exams, the bi-parametric baseline achieved a ROC AUC of 0.89, showing a higher specificity that clinical PI-RADS at a sensitivity of 0.9. Additional improvement could be achieved by fusing bpMRI and DCE predictions, resulting in a ROC AUC of 0.9.

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