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Abstract #1515

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

Nils Bastian Netzer1,2, Adrian Schrader1,2, 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


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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