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Article

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept

by
David J. Winkel
1,2,*,†,
Christian Wetterauer
3,†,
Marc Oliver Matthias
3,
Bin Lou
2,
Bibo Shi
2,
Ali Kamen
2,
Dorin Comaniciu
2,
Hans-Helge Seifert
3,
Cyrill A. Rentsch
3 and
Daniel T. Boll
1
1
Department of Radiology, University Hospital of Basel, 4051 Basel, Basel-Stadt, Switzerland
2
Siemens Healthineers, Medical Imaging Technologies Princeton, Princeton, NJ 08540, USA
3
Department of Urology, University Hospital of Basel, 4051 Basel, Basel-Stadt, Switzerland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2020, 10(11), 951; https://doi.org/10.3390/diagnostics10110951
Submission received: 7 September 2020 / Revised: 27 October 2020 / Accepted: 11 November 2020 / Published: 14 November 2020
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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Abstract

Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports—serving as the ground truth—was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.
Keywords: prostatic neoplasms; early detection of cancer; magnetic resonance imaging; deep learning prostatic neoplasms; early detection of cancer; magnetic resonance imaging; deep learning

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MDPI and ACS Style

Winkel, D.J.; Wetterauer, C.; Matthias, M.O.; Lou, B.; Shi, B.; Kamen, A.; Comaniciu, D.; Seifert, H.-H.; Rentsch, C.A.; Boll, D.T. Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept. Diagnostics 2020, 10, 951. https://doi.org/10.3390/diagnostics10110951

AMA Style

Winkel DJ, Wetterauer C, Matthias MO, Lou B, Shi B, Kamen A, Comaniciu D, Seifert H-H, Rentsch CA, Boll DT. Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept. Diagnostics. 2020; 10(11):951. https://doi.org/10.3390/diagnostics10110951

Chicago/Turabian Style

Winkel, David J., Christian Wetterauer, Marc Oliver Matthias, Bin Lou, Bibo Shi, Ali Kamen, Dorin Comaniciu, Hans-Helge Seifert, Cyrill A. Rentsch, and Daniel T. Boll. 2020. "Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept" Diagnostics 10, no. 11: 951. https://doi.org/10.3390/diagnostics10110951

APA Style

Winkel, D. J., Wetterauer, C., Matthias, M. O., Lou, B., Shi, B., Kamen, A., Comaniciu, D., Seifert, H.-H., Rentsch, C. A., & Boll, D. T. (2020). Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept. Diagnostics, 10(11), 951. https://doi.org/10.3390/diagnostics10110951

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