Evaluation of prostate MRI: can machine learning provide support where radiologists need it?

Excerpt

Prostate cancer (PCa) is a heterogeneous disease and tumor grading is a major predictor of prognosis [1]. Multiparametric magnetic resonance imaging (mpMRI) is a valuable tool to non-invasively detect PCa and has even proven superior compared to systematic biopsy [2]. In order to improve and standardize this technique and its application, guidelines for acquisition, interpretation, and reporting of mpMRI of the prostate have been established [3]. Using a current version of these guidelines, the Prostate Imaging–Reporting and Data System (PI-RADS) version 2, and its decision rules, high sensitivities and specificities for the detection of clinically significant PCa can be reached [4]. A major limitation is an only moderate to good interreader agreement [5]. In order to address this issue, an updated version of PI-RADS, version 2.1, has been published just recently [6]. …