Deep Learning Role in Early Diagnosis of Prostate Cancer

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b v...

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Vydáno v:	Technology in cancer research & treatment Ročník 17; s. 1533034618775530
Hlavní autoři:	Reda, Islam, Khalil, Ashraf, Elmogy, Mohammed, Abou El-Fetouh, Ahmed, Shalaby, Ahmed, Abou El-Ghar, Mohamed, Elmaghraby, Adel, Ghazal, Mohammed, El-Baz, Ayman
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Angeles, CA SAGE Publications 01.01.2018 Sage Publications Ltd
Témata:	Algorithms Antigens Deep Learning Diffusion Magnetic Resonance Imaging Early Detection of Cancer - methods Humans Image Interpretation, Computer-Assisted Male NMR Nuclear magnetic resonance Prostate cancer Prostatic Neoplasms - diagnosis Reproducibility of Results ROC Curve Sensitivity and Specificity Special Collection on Deep Learning in Molecular Imaging–Research Paper ADC prostate cancer SNCSAE CAD PSA
ISSN:	1533-0346, 1533-0338, 1533-0338
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Abstract	The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.
AbstractList	The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system. The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen-based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient-cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen-based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient-cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.
Author	Abou El-Fetouh, Ahmed El-Baz, Ayman Elmogy, Mohammed Abou El-Ghar, Mohamed Elmaghraby, Adel Khalil, Ashraf Ghazal, Mohammed Reda, Islam Shalaby, Ahmed
AuthorAffiliation	2 Department of Bioengineering, University of Louisville, Louisville, KY, USA 3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates 5 Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA 1 Faculty of Computers and Information, Mansoura University, Mansoura, Egypt 4 Radiology Department, Mansoura University, Mansoura, Egypt
AuthorAffiliation_xml	– name: 3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates – name: 2 Department of Bioengineering, University of Louisville, Louisville, KY, USA – name: 5 Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA – name: 1 Faculty of Computers and Information, Mansoura University, Mansoura, Egypt – name: 4 Radiology Department, Mansoura University, Mansoura, Egypt
Author_xml	– sequence: 1 givenname: Islam surname: Reda fullname: Reda, Islam – sequence: 2 givenname: Ashraf surname: Khalil fullname: Khalil, Ashraf – sequence: 3 givenname: Mohammed surname: Elmogy fullname: Elmogy, Mohammed – sequence: 4 givenname: Ahmed surname: Abou El-Fetouh fullname: Abou El-Fetouh, Ahmed – sequence: 5 givenname: Ahmed surname: Shalaby fullname: Shalaby, Ahmed – sequence: 6 givenname: Mohamed surname: Abou El-Ghar fullname: Abou El-Ghar, Mohamed – sequence: 7 givenname: Adel surname: Elmaghraby fullname: Elmaghraby, Adel – sequence: 8 givenname: Mohammed surname: Ghazal fullname: Ghazal, Mohammed – sequence: 9 givenname: Ayman surname: El-Baz fullname: El-Baz, Ayman
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29804518$$D View this record in MEDLINE/PubMed
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Keywords	ADC prostate cancer SNCSAE CAD PSA
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Snippet	The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both...
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SubjectTerms	Algorithms Antigens Deep Learning Diffusion Magnetic Resonance Imaging Early Detection of Cancer - methods Humans Image Interpretation, Computer-Assisted Male NMR Nuclear magnetic resonance Prostate cancer Prostatic Neoplasms - diagnosis Reproducibility of Results ROC Curve Sensitivity and Specificity Special Collection on Deep Learning in Molecular Imaging–Research Paper
Title	Deep Learning Role in Early Diagnosis of Prostate Cancer
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