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Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network

Abstract

Objective

To present a deep learning–based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network (CNN).

Methods

Two hundred patients with a total of 318 lesions for which histological correlation was available were analyzed. A novel CNN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted, apparent diffusion coefficient (ADC), diffusion-weighted images, and K-trans) and the effect of different sequences on the network’s performance was tested and discussed. The particular choice of modeling approach was justified by testing all relevant data combinations. The model was trained and validated using eightfold cross-validation.

Results

In terms of detection of significant prostate cancer defined by biopsy results as the reference standard, the 3D CNN achieved an area under the curve (AUC) of the receiver operating characteristics ranging from 0.89 (88.6% and 90.0% for sensitivity and specificity respectively) to 0.91 (81.2% and 90.5% for sensitivity and specificity respectively) with an average AUC of 0.897 for the ADC, DWI, and K-trans input combination. The other combinations scored less in terms of overall performance and average AUC, where the difference in performance was significant with a p value of 0.02 when using T2w and K-trans; and 0.00025 when using T2w, ADC, and DWI. Prostate cancer classification performance is thus comparable to that reported for experienced radiologists using the prostate imaging reporting and data system (PI-RADS). Lesion size and largest diameter had no effect on the network’s performance.

Conclusion

The diagnostic performance of the 3D CNN in detecting clinically significant prostate cancer is characterized by a good AUC and sensitivity and high specificity.

Key Points

• Prostate cancer classification using a deep learning model is feasible and it allows direct processing of MR sequences without prior lesion segmentation.

• Prostate cancer classification performance as measured by AUC is comparable to that of an experienced radiologist.

• Perfusion MR images (K-trans), followed by DWI and ADC, have the highest effect on the overall performance; whereas T2w images show hardly any improvement.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CNN:

Convolutional neural network

DCE:

Dynamic contrast-enhanced

DWI:

Diffusion-weighted imaging

Mp-MRI:

Multi-parametric magnetic resonance imaging

MR:

Magnetic resonance

PCa:

Prostate cancer

PI-RADS:

Prostate imaging reporting and data system

PSA:

Prostate-specific antigen

ROC:

Receiver operating characteristics

T2w:

T2-weighted

TRUS:

Transrectal ultrasound

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Funding

This work was funded by the German Research Foundation (Graduate Program BIOQIC, GRK2260, BIOQIC) and the Berlin Institute of Health within the Clinician Scientist Program (TP).

Author information

Correspondence to Nader Aldoj or Marc Dewey.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Prof. Dr. Marc Dewey.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Nader Aldoj is a PhD student supported by a graduate program of the German Research Foundation (GRK2260, BIOQIC). Prof. Dewey (a PI of BIOQIC funded by the German Research Foundation, GRK2260) has also received grant support from the Heisenberg Program of the DFG for a professorship (DE 1361/14-1) and the FP7 Program of the European Commission for the randomized multi-center DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2). Prof. Dewey is a cardiac section editor of European Radiology. Prof. Dewey has received lecture fees from Toshiba Medical Systems, Guerbet, Cardiac MR Academy Berlin, and Bayer (Schering-Berlex). Tobias Penzkofer received research support from Siemens Healthcare and Philips Healthcare. Tobias Penzkofer received grant support from the Berlin Institute of Health within the Clinician Scientist Program. Outside of the current work, Tobias Penzkofer is involved in clinical trials with AGO, Aprea AB, Astellas Pharma Global Inc., AstraZeneca, Celgene, Genmab A/S, Incyte Corporation, Lion Biotechnologies, Inc., Millennium Pharmaceuticals, Inc., Morphotec Inc., MSD Tesaro Inc., and Roche. Institutional master research agreements exist with Siemens Medical Solutions, Philips Medical Systems, and Toshiba Medical Systems. The terms of these arrangements are managed by the legal department of Charité – Universitätsmedizin Berlin. The other authors declared no conflicts of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

No consent was necessary. The dataset is publicly available.

Ethical approval

No ethical approval was necessary.

Methodology

• Retrospective

• Performed at one institution

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Aldoj, N., Lukas, S., Dewey, M. et al. Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network. Eur Radiol 30, 1243–1253 (2020). https://doi.org/10.1007/s00330-019-06417-z

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Keywords

  • Three-dimensional images
  • Prostate cancer
  • Multi-parametric MRI
  • Convolutional neural networks
  • Deep learning