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Abstract #1514

Prostate Cancer Risk Assessment using Fully Automatic Deep Learning in MRI: Integration with Clinical Data using Logistic Regression Models

Adrian Schrader1,2, Nils Bastian Netzer1,2, Magdalena Görtz3, Constantin Schwab4, Markus Hohenfellner3, Heinz-Peter Schlemmer1, and David Bonekamp1
1Division of Radiology, German Cancer Research Center, Heidelberg, Germany, 2Heidelberg University Medical School, Heidelberg, Germany, 3Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany, 4Department of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany


For patients with clinical suspicion for significant prostate cancer, the decision to undergo prostate biopsy can be supported by calculating the individual risk profile using demographic and clinical information along with multiparametric MRI assessment. We could show that the prediction performance of an established risk calculator remained stable after substituting manual PI-RADS scores for assessments from a fully automated deep learning system. Combining deep learning and PI-RADS resulted in significant improvements over using only PI-RADS. Complementary information that deep learning models are able to extract enable synergies with radiologists to improve individual risk predictions.

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