Abstract #3525

Prediction of prostate cancer aggressiveness using open-source machine learning tools for 5-minute prostate MRI: PRODIF CAD 1.0

Harri Merisaari^1,2,3, Pekka Taimen⁴, Otto Ettala⁵, Marko Pesola¹, Jani Saunavaara⁶, Anant Madabhushi³, Peter J Boström⁵, Hannu Aronen^1,6, and Ivan Jambor^1,7

¹Department of Diagnostic Radiology, University of Turku, Turku, Finland, ²Department of Future Technologies, University of Turku, Turku, Finland, ³Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ⁴Department of Pathology, Institute of Biomedicine, Turku, Finland, ⁵Department of Urology, Turku University Hospital, Turku, Finland, ⁶Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland, ⁷Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Acquisition time of a routine prostate MRI can be up to 20-25 minutes leading to significant financial burden on healthcare systems as the number of prostate MRI continue to increase. We developed, validated and tested an open-source radiomics/texture tools for 5-minute biparametric prostate MRI (T2-weighed imaging and DWI obtained using 4 b-values (0, 900, 1100, 2000 s/mm2)) using whole mount prostatectomy sections of 157 men with prostate cancer, PCa, (244 PCa lesions). Best features were corner detectors with AUC (clinically insignificant vs insignificant prostate cancer) in the range of 0.82-0.89. Code and data are available at: https://github.com/haanme/ProstateFeatures and http://mrc.utu.fi/data .

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