Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study
Open accessAbstract
Purpose This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer.Methods We use deep learning to analyze temporal ultrasound data obtained from 255 cancer foci identified in mp-MRI. Each target is sampled in axial and sagittal planes. A deep belief network is trained to automatically learn the high-level latent features of temporal ultrasound data. A support vector machine classifier is then applied to differentiate cancerous versus benign tissue, verified by histopathology. Data from 32 targets are used for the training, while the remaining 223 targets are used for testing.Results Our results indicate that the distance between the biopsy target and the prostate boundary, and the agreement between axial and sagittal histopathology of each target impact the classification accuracy. In 84 test cores that are 5 mm or farther to the prostate boundary, and have consistent pathology outcomes in axial and sagittal biopsy planes, we achieve an area under the curve of 0.80. In contrast, all of these targets were labeled as moderately suspicious in mp-MR.Conclusion Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.
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Azizi et al. successfully accomplished detection and grading of prostate cancer through a combination of high-level abstract features extracted from temporal-enhanced US by a DBN and tissue structure from digital pathology.
“Recently, Azizi et al. [20,106,107] successfully accomplished the detection and grading of prostate cancer through a combination of high-level abstract features extracted from temporal-enhanced US by a DBN and the structure of tissue from digital pathology.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [106]
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- Authors
- Azizi, S., Imani, F., Ghavidel, S., Tahmasebi, A., Kwak, J. T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Mousavi, P., & Abolmaesumi, P.
- Affiliations
- University of British Columbia (CA)
- Queen's University (CA)
- National Institutes of Health (US)
- Corresponding author
- Azizi, Shekoofeh
- ORCID
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2016-04-08
- Volume
- 11
- Issue
- 6
- Pages
- 947-956
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- PMC
- PMC6364694
- OpenAlex
- W2317789088
- Cited by
- 45
- References
- 33
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 6 · 2020: 9 · 2019: 5 · 2018: 7 · 2017: 5 · 2016: 1
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- prostate cancer detection, multiparametric MRI, deep belief network, support vector machine, histopathology, unnecessary biopsy
- MeSH terms
- Aged, Feasibility Studies, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prostate, Prostatic Neoplasms, Ultrasonography, Image-Guided Biopsy
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