Prostate cancer detection using deep convolutional neural networks

Yoo, Sunghwan; Gujrathi, Isha; Haider, Masoom A.; Khalvati, Farzad

doi:10.1038/s41598-019-55972-4

Cited by 232AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Prostate cancer detection using deep convolutional neural networks

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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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End-to-end convolutional neural networks have dominated performance at diverse medical imaging tasks.
“Motivated by the dominant performance of CNNs, it is desirable to develop an end-to-end network for automatic placental maturity grading (PMG) from ultrasound images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [17]
The Introduction describes promising CNN results in computer vision, and the Discussion reports that deep learning has outperformed radiomics-based methods for prostate-cancer classification. These passages do not establish dominant performance across diverse medical-imaging tasks. The Conclusion also describes this paper’s pipeline as two-step rather than end-to-end. No page numbers are available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Convolutional neural networks have been used successfully in medical imaging applications including mammography, brain imaging, and prostate cancer detection.
“They have been used successfully in a variety of medical imaging applications, including mammography [15], brain imaging [16], and prostate cancer detection [17].”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [17]

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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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Prostate cancer detection using deep convolutional neural networks

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