© 2016 Roethke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Objective: To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate. Methods: A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI) that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies. Results: In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0), a specificity of 87.5% (with 95% CI of 69.0-95.7) and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8) for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature. Conclusion: The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.
Roethke, M. C., Kuru, T. H., Mueller-Wolf, M. B., Agterhuis, E., Edler, C., Hohenfellner, M., … Hadaschik, B. A. (2016). Evaluation of an automated analysis tool for prostate cancer prediction using multiparametric magnetic resonance imaging. PLoS ONE, 11(7). https://doi.org/10.1371/journal.pone.0159803