Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations
Open accessAbstract
PURPOSE : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue. RESULTS : Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization. CONCLUSION : Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [107]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 1,592 · see citing works
Siddiqui, M. M., Rais‐Bahrami, S., Türkbey, B., George, A. K., Rothwax, J. T., Shakir, N. A., Okoro, C., Raskolnikov, D., Parnes, H. L., Linehan, W. M., et al. (2015) · JAMA · doi:10.1001/jama.2014.17942 · Cited by 1,503 · see citing works
Self-citationOn Convergence Properties of the EM Algorithm for Gaussian Mixtures
Xu, L., & Jordan, M. I. (1996) · Neural Computation · doi:10.1162/neco.1996.8.1.129 · Cited by 763 · see citing works
Epstein, J. I., Feng, Z., Trock, B. J., et al. (2012) · European Urology · doi:10.1016/j.eururo.2012.01.050 · Cited by 669 · see citing works
Accuracy of Multiparametric MRI for Prostate Cancer Detection: A Meta-Analysis
de Rooij, M., Hamoen, E. H. J., Fütterer, J. J., et al. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.13.11046 · Cited by 470 · see citing works
Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies
Xu, S., Kruecker, J., Türkbey, B., et al. (2008) · Computer Aided Surgery · doi:10.3109/10929080802364645 · Cited by 296 · see citing works
Self-citationMRI–ultrasound fusion for guidance of targeted prostate biopsy
Marks, L. S., Young, S., & Natarajan, S. (2012) · Current Opinion in Urology · doi:10.1097/mou.0b013e32835ad3ee · Cited by 250 · see citing works
Iczkowski, K. A., Torkko, K. C., Kotnis, G. R., et al. (2011) · American Journal of Clinical Pathology · doi:10.1309/ajcpz7wbu9yxsjpe · Cited by 209 · see citing works
Self-citationTürkbey, B., Mani, H., Aras, Ö., Ho, J., Hoang, A., Rastinehad, A. R., Agarwal, H. K., Shah, V. P., Bernardo, M., Pang, Y., et al. (2013) · Radiology · doi:10.1148/radiol.13121325 · Cited by 205 · see citing works
Self-citationKuru, T. H., Roethke, M. C., Seidenader, J., et al. (2013) · The Journal of Urology · doi:10.1016/j.juro.2013.04.043 · Cited by 188 · see citing works
Simulation of advanced ultrasound systems using Field II
Jensen, J. A. (2005) · doi:10.1109/isbi.2004.1398618 · Cited by 176 · see citing works
Prostate Cancer: Diagnostic Performance of Real-time Shear-Wave Elastography
Corréas, J.-M., Tissier, A.-M., Khairoune, A., et al. (2014) · Radiology · doi:10.1148/radiol.14140567 · Cited by 166 · see citing works
Nelson, E. D., Slotoroff, C. B., Gomella, L. G., et al. (2007) · Urology · doi:10.1016/j.urology.2007.07.067 · Cited by 159 · see citing works
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
Ma, J., Xu, L., & Jordan, M. I. (2000) · Neural Computation · doi:10.1162/089976600300014764 · Cited by 120 · see citing works
Moradi, M., Abolmaesumi, P., Siemens, D. R., et al. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2009766 · Cited by 92 · see citing works
Self-citationHUNT, J. W., Worthington, A. E., Xuan, A., et al. (2002) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(01)00494-x · Cited by 83 · see citing works
Computer-aided detection of prostate cancer
Llobet, R., Pérez-Cortés, J.-C., Toselli, A. H., et al. (2006) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.03.001 · Cited by 66 · see citing works
A feasibility study of MR elastography in the diagnosis of prostate cancer at 3.0T
Li, S.-Y., Chen, M., Wang, W., et al. (2011) · Acta Radiologica · doi:10.1258/ar.2010.100276 · Cited by 64 · see citing works
HUNT, J. W., Worthington, A. E., & Kerr, A. (1995) · Ultrasound in Medicine & Biology · doi:10.1016/0301-5629(94)00120-3 · Cited by 62 · see citing works
Tissue typing using ultrasound RF time series: Experiments with animal tissue samples
Moradi, M., Abolmaesumi, P., & Mousavi, P. (2010) · Medical Physics · doi:10.1118/1.3457710 · Cited by 49 · see citing works
Self-citationActive surveillance for prostate cancer
Singer, E. A., Kaushal, A., Türkbey, B., Couvillon, A., Pinto, P. A., & Parnes, H. L. (2012) · Current Opinion in Oncology · doi:10.1097/cco.0b013e3283527f99 · Cited by 48 · see citing works
Self-citationComputer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study
Imani, F., Abolmaesumi, P., Gibson, E., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2427739 · Cited by 45 · see citing works
Self-citationAzizi, S., Imani, F., Ghavidel, S., Tahmasebi, A., Kwak, J. T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., et al. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1395-2 · Cited by 45 · see citing works
Self-citationTissue Classification Using Ultrasound-Induced Variations in Acoustic Backscattering Features
Daoud, M. I., Mousavi, P., Imani, F., et al. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2224111 · Cited by 31 · see citing works
Self-citationAzizi, S., Imani, F., Zhuang, B., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_9 · Cited by 31 · see citing works
Self-citationRecent Advances in Ultrasonic Tissue-Type Imaging of the Prostate
Feleppa, E. J., Porter, C. R., Ketterling, J. A., et al. (2007) · Acoustical imaging · doi:10.1007/1-4020-5721-0_35 · Cited by 26 · see citing works
Ultrasound RF time series for tissue typing: firstin vivoclinical results
Moradi, M., Mahdavi, S. S., Nir, G., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2007672 · Cited by 23 · see citing works
Self-citationUltrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis
Imani, F., Ramezani, M., Nouranian, S., et al. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2404300 · Cited by 18 · see citing works
Characterization of aggressive prostate cancer using ultrasound RF time series
Khojaste, A., Imani, F., Moradi, M., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082663 · Cited by 12 · see citing works
Imani, F., Zhuang, B., Tahmasebi, A. M., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1184-3 · Cited by 11 · see citing works
Self-citationClassifying Cancer Grades Using Temporal Ultrasound for Transrectal Prostate Biopsy
Azizi, S., Imani, F., Kwak, J. T., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_76 · Cited by 10 · see citing works
Self-citationDaoud, M. I., & Lacefield, J. C. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877855 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846 · see citing works
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal
Xie, H. N., Wang, N., He, M., Zhang, L. H., Cai, H. M., Xian, J. B., Lin, M. F., Zheng, J., & Yang, Y. Z. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.21967 · Cited by 176 · see citing works
AI and professional liability assessment in healthcare. a revolution in legal medicine?
Terranova, C., Cestonaro, C., Fava, L., & Cinquetti, A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1337335 · Cited by 73 · see citing works
- Authors
- Azizi, S., Bayat, S., Yan, P., Tahmasebi, A., Nir, G., Kwak, J. T., Xu, S., Wilson, S., Iczkowski, K. A., Lucia, M. S., Goldenberg, L., Salcudean, S. E., Pinto, P. A., Wood, B., Abolmaesumi, P., & Mousavi, P.
- Affiliations
- University of British Columbia (CA)
- Sejong University (KR)
- National Institutes of Health (US)
- University of Colorado Denver (US)
- Vancouver Prostate Centre (CA)
- Queen's University (CA)
- Corresponding author
- Azizi, Shekoofeh
- Abolmaesumi, Purang
- Mousavi, Parvin
- ORCID
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2017-06-20
- Volume
- 12
- Issue
- 8
- Pages
- 1293-1305
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- PMC
- PMC7900902
- OpenAlex
- W2632480913
- Cited by
- 47
- References
- 34
- Citations by year
- 2024: 6 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 12 · 2019: 7 · 2018: 6 · 2017: 1
- FWCI
- 5.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- 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, cancer grading, deep neural network, feature visualization, distribution learning, tissue microstructure, digital pathology
- MeSH terms
- Humans, Magnetic Resonance Imaging, Male, Neoplasm Staging, Prostatic Neoplasms, Sensitivity and Specificity, Neural Networks, Computer, Ultrasonography, Interventional, Imaging, Three-Dimensional, Image-Guided Biopsy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
