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Prostate Cancer Detection using Deep Convolutional Neural Networks

Open access
Yoo, Sunghwan, Gujrathi, Isha, Haider, Masoom A., Khalvati, Farzad·2019·Scientific Reports
DOI 10.1038/s41598-019-55972-4PMC PMC6925141
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Abstract

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95[Formula: see text] Confidence Interval (CI): 0.84-0.90) and 0.84 (95[Formula: see text] CI: 0.76-0.91) at slice level and patient level, respectively.

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Figure 1 Block diagram of the proposed pipeline for prostate cancer detection. The inputs to each CNN are 66 × 66 × 6 (ADC, b0, b100, b400, b1000, b1600) MRI slices. The output is the slice level and patient level results.
Figure 1 Block diagram of the proposed pipeline for prostate cancer detection. The inputs to each CNN are 66 × 66 × 6 (ADC, b0, b100, b400, b1000, b1600) MRI slices. The output is the slice level and patient level results.CC BY · PubMed Central
Figure 2 The structural difference between original residual network and fully pre-activated residual network.
Figure 2 The structural difference between original residual network and fully pre-activated residual network.CC BY · PubMed Central
Figure 3 Block diagram of the proposed first-order statistical feature extractor. PCa Set: probabilistic output set from each CNN which is associated with PCa class. Non PCa Set: probabilistic output set from each CNN which is associated with non PCa class.
Figure 3 Block diagram of the proposed first-order statistical feature extractor. PCa Set: probabilistic output set from each CNN which is associated with PCa class. Non PCa Set: probabilistic output set from each CNN which is associated with non PCa class.CC BY · PubMed Central
Figure 4 Slice-level ROC curve of the proposed ResNet inspired deep learning architecture (AUC: 0.87, CI: 0.84–0.90).
Figure 4 Slice-level ROC curve of the proposed ResNet inspired deep learning architecture (AUC: 0.87, CI: 0.84–0.90).CC BY · PubMed Central
Figure 5 Patient-level ROC curve of the proposed pipeline: Random Forest classifier trained on the features extracted by the CNNs (AUC: 0.84, CI: 0.76–0.91).
Figure 5 Patient-level ROC curve of the proposed pipeline: Random Forest classifier trained on the features extracted by the CNNs (AUC: 0.84, CI: 0.76–0.91).CC BY · PubMed Central

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Show all 39 references

Research works printed but not linked: 4

  1. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M. Machine learning: An artificial intelligence approach (Springer Science & Business Media, 2013).
  2. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. nature 521, 436 (2015). · DOI · Paper page
  3. Vapnik, V.The nature of statistical learning theory (Springer science and business media, 2013).
  4. Brinkmann, R.The art and science of digital compositing: Techniques for visual effects, animation and motion graphics (Morgan Kaufmann, 2008).

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Metadata

Authors
Yoo, S., Gujrathi, I., Haider, M. A., & Khalvati, F.
Affiliations
  • Lunenfeld-Tanenbaum Research Institute (CA)
  • University of Toronto (CA)
Corresponding author
Khalvati, Farzad
ORCID
Haider, Masoom A.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-12-20
Volume
9
Issue
1
Pages
19518-19518
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-55972-4
PMC
PMC6925141
OpenAlex
W2995218128
Cited by
230
References
39
Citations by year
2026: 14 · 2025: 31 · 2024: 37 · 2023: 36 · 2022: 42 · 2021: 50 · 2020: 17 · 2019: 3
FWCI
12.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Ontario Institute for Cancer Research
  • Government of Ontario
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords
prostate cancer detection, clinically significant prostate cancer, diffusion-weighted imaging, computer-aided detection, deep convolutional neural networks, medical image classification
MeSH terms
Deep Learning, Humans, Male, Prostatic Neoplasms, ROC Curve, Neural Networks, Computer, Diffusion Magnetic Resonance Imaging

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