Abstract #3483

Machine learning for prostate cancer Gleason score prediction using radiomics of T2-weighted imaging, diffusion weighted imaging and T2-mapping

Jussi Toivonen^1,2, Ileana Montoya Perez^1,2, Parisa Movahedi^1,2, Harri Merisaari^1,2, Janne Verho², Pekka Taimen³, Peter J Boström⁴, Tapio Pahikkala¹, Hannu J Aronen², and Ivan Jambor^1,2

¹Department of Future Technologies, University of Turku, Turku, Finland, ²Department of Diagnostic Radiology, University of Turku, Turku, Finland, ³Department of Pathology, University of Turku, Turku, Finland, ⁴Department of Urology, Turku University Hospital, Turku, Finland

We extensively evaluated large number radiomics of prostate T2-weighted imaging, diffusion weighted imaging and T2-mapping. The highest overall performance estimate (AUC = 0.88) we obtained for the model utilizing a small subset of texture features from the ADCm, K, and T2w parameters. These features included texture descriptors based on gray-level co-occurrence matrix, Gabor transform, and the Zernike and Hu moments.

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