Prostate cancer (PCa) is currently the second leading cause of cancer-related death in men. Systematic biopsies are the standard of care for PCa diagnosis. However, biopsies are invasive and prone to sampling errors. With magnetic resonance imaging (MRI) the whole prostate tissue can be visualized non-invasively. In this study we evaluate a radiomics approach to classify suspected lesions, into highgrade and low-grade PCa. The data comprised MRI, histology of radical prostatectomy specimens and pathology reports of 40 patients. Histology and MRI were correlated obtaining 72 lesions. Features were extracted to train a Support Vector Machine as classifier. Our experiments were performed in a fully automated framework, using 100x random split cross-validation, including extensive algorithm selection and hyperparameter optimization on the training set in each cross-validation. Our method achieved an AUC of 77[0.66-0.87], sensitivity 0.74[0.57-0.91] and specificity of 0.66[0.50-0.82], demonstrating the potential of radiomics to classify PCa lesion based on MRI.

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Keywords Classification, Computer aided diagnosis, Gleason score, Machine learning, Magnetic resonance imaging, Prostate cancer, Radiomics
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Conference 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
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Castillo, T.J.M. (T. Jose M.), Starmans, M.P.A, Niessen, W.J, Schoots, I.G, Klein, S, & Veenland, J.F. (2019). Classification of prostate cancer: High grade versus low grade using a radiomics approach. In Proceedings - International Symposium on Biomedical Imaging (pp. 1319–1322). doi:10.1109/ISBI.2019.8759217