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

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2016 Jul 26

PMID 27454770

Citations 8

Authors

Matthias C Roethke

Timur H Kuru

Maya B Mueller-Wolf

Erik Agterhuis

Christopher Edler

Markus Hohenfellner

Heinz-Peter Schlemmer

Boris A Hadaschik

Affiliations

Soon will be listed here.

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.

Citing Articles

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PI-RADS: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI.

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Prostate Cancer Detection with mpMRI According to PI-RADS v2 Compared with Systematic MRI/TRUS-Fusion Biopsy: A Prospective Study.

Sauck A, Keller I, Hainc N, Pfofe D, Najafi A, John H Tomography. 2022; 8(4):2020-2029.

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Diagnostic accuracy of different computer-aided diagnostic systems for prostate cancer based on magnetic resonance imaging: A systematic review with diagnostic meta-analysis.

Xing X, Zhao X, Wei H, Li Y Medicine (Baltimore). 2021; 100(3):e23817.

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