Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging

Matthias C Roethke, Timur H Kuru, Maya B Mueller-Wolf, Erik Agterhuis, Christopher Edler, Markus Hohenfellner, Heinz-Peter Schlemmer, Boris A Hadaschik, Matthias C Roethke, Timur H Kuru, Maya B Mueller-Wolf, Erik Agterhuis, Christopher Edler, Markus Hohenfellner, Heinz-Peter Schlemmer, Boris A Hadaschik

Abstract

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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Example of a MAI map…
Fig 1. Example of a MAI map on a T2W image overlay.
Fig 2. Examples of MAI profiles.
Fig 2. Examples of MAI profiles.
Confirmed Gleason score 7a (3+4, left) and Gleason score 8 (4+4, right) lesions.
Fig 3. Overview of MAI algorithm steps.
Fig 3. Overview of MAI algorithm steps.
Fig 4. ROC plot of MAI diagnostic…
Fig 4. ROC plot of MAI diagnostic accuracy.
Fig 5. Comparison of computed MAI diagnostic…
Fig 5. Comparison of computed MAI diagnostic accuracy with that of human readers based on PI-RADS scoring.

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