Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging
Purpose: There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prost...
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| Veröffentlicht in: | Medical physics (Lancaster) 2012-07, Vol.39 (7), p.4093-4103 |
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| creator | Shah, Vijay Turkbey, Baris Mani, Haresh Pang, Yuxi Pohida, Thomas Merino, Maria J. Pinto, Peter A. Choyke, Peter L. Bernardo, Marcelino |
| description | Purpose:
There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).
Methods:
This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.
Results:
For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.
Conclusions:
This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps |
| doi_str_mv | 10.1118/1.4722753 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_scitation_primary_10_1118_1_4722753</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1030075834</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4493-ac88aa0d497c6985a1c0a3b2f2572873b08d2821adaad8857ce8f58ca3de9a593</originalsourceid><addsrcrecordid>eNp9kU1vFDEMhiMEotvCgT-AckRIU5yvJnNBQoUCUqtygHPkzWSWVDOTIckULb-erHap2kM52bIfv7b8EvKKwSljzLxjp1JzrpV4QlZcatFIDu1TsgJoZcMlqCNynPMNAJwJBc_JEedGgJZ8RW4-ehdyiBPNyzzHVGje5uJH2sdEh-hwCH_CtKFzirlg8dTh5Hyia8y-o3VsXIYSZkw4-pKCoyNuJl9qknyO0w6modaqxgvyrMch-5eHeEJ-XHz6fv6lubz-_PX8w2XjpGxFg84YROhkq91ZaxQyByjWvOdKc6PFGkzHDWfYIXbGKO286ZVxKDrfomrFCXm_152X9eg756eScLBzqnekrY0Y7MPOFH7aTby1QrQA0lSBNweBFH8tPhc7huz8MODk45ItAwGglRGyoq_v77pb8u_BFWj2wO8w-O1dn4HdOWeZPThnr77tQuXf7vnsQv13Nebxmf_BtzHdE5-7XvwFyR2pYA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1030075834</pqid></control><display><type>article</type><title>Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging</title><source>MEDLINE</source><source>Wiley Journals</source><source>Alma/SFX Local Collection</source><creator>Shah, Vijay ; Turkbey, Baris ; Mani, Haresh ; Pang, Yuxi ; Pohida, Thomas ; Merino, Maria J. ; Pinto, Peter A. ; Choyke, Peter L. ; Bernardo, Marcelino</creator><creatorcontrib>Shah, Vijay ; Turkbey, Baris ; Mani, Haresh ; Pang, Yuxi ; Pohida, Thomas ; Merino, Maria J. ; Pinto, Peter A. ; Choyke, Peter L. ; Bernardo, Marcelino</creatorcontrib><description>Purpose:
There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).
Methods:
This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.
Results:
For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.
Conclusions:
This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accuratelylocalizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>EISSN: 0094-2405</identifier><identifier>DOI: 10.1118/1.4722753</identifier><identifier>PMID: 22830742</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>Aged ; Algorithms ; Biomedical modeling ; biomedical MRI ; Cancer ; Clinical applications ; Cluster analysis ; Computer software ; Computer systems utilizing knowledge based models ; decision support system ; decision support systems ; Decision Support Systems, Clinical ; Digital computing or data processing equipment or methods, specially adapted for specific applications ; genetic algorithms ; Humans ; Image data processing or generation, in general ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; image segmentation ; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines ; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging ; learning (artificial intelligence) ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Magnetic Resonance Physics ; Male ; medical computing ; medical image processing ; Medical image segmentation ; Medical imaging ; Medical magnetic resonance imaging ; Middle Aged ; multiparametric MRI ; multispectral MRI ; Pattern Recognition, Automated - methods ; probability ; prostate cancer localization ; Prostatic Neoplasms - diagnosis ; Radiologists ; Reproducibility of Results ; Segmentation ; Sensitivity and Specificity ; support vector machine ; support vector machines ; Tissues ; tumours</subject><ispartof>Medical physics (Lancaster), 2012-07, Vol.39 (7), p.4093-4103</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2012 American Association of Physicists in Medicine</rights><rights>Copyright © 2012 American Association of Physicists in Medicine 2012 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4493-ac88aa0d497c6985a1c0a3b2f2572873b08d2821adaad8857ce8f58ca3de9a593</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1118%2F1.4722753$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.4722753$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22830742$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shah, Vijay</creatorcontrib><creatorcontrib>Turkbey, Baris</creatorcontrib><creatorcontrib>Mani, Haresh</creatorcontrib><creatorcontrib>Pang, Yuxi</creatorcontrib><creatorcontrib>Pohida, Thomas</creatorcontrib><creatorcontrib>Merino, Maria J.</creatorcontrib><creatorcontrib>Pinto, Peter A.</creatorcontrib><creatorcontrib>Choyke, Peter L.</creatorcontrib><creatorcontrib>Bernardo, Marcelino</creatorcontrib><title>Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose:
There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).
Methods:
This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.
Results:
For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.
Conclusions:
This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accuratelylocalizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.</description><subject>Aged</subject><subject>Algorithms</subject><subject>Biomedical modeling</subject><subject>biomedical MRI</subject><subject>Cancer</subject><subject>Clinical applications</subject><subject>Cluster analysis</subject><subject>Computer software</subject><subject>Computer systems utilizing knowledge based models</subject><subject>decision support system</subject><subject>decision support systems</subject><subject>Decision Support Systems, Clinical</subject><subject>Digital computing or data processing equipment or methods, specially adapted for specific applications</subject><subject>genetic algorithms</subject><subject>Humans</subject><subject>Image data processing or generation, in general</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>image segmentation</subject><subject>In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines</subject><subject>Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging</subject><subject>learning (artificial intelligence)</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Magnetic Resonance Physics</subject><subject>Male</subject><subject>medical computing</subject><subject>medical image processing</subject><subject>Medical image segmentation</subject><subject>Medical imaging</subject><subject>Medical magnetic resonance imaging</subject><subject>Middle Aged</subject><subject>multiparametric MRI</subject><subject>multispectral MRI</subject><subject>Pattern Recognition, Automated - methods</subject><subject>probability</subject><subject>prostate cancer localization</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Radiologists</subject><subject>Reproducibility of Results</subject><subject>Segmentation</subject><subject>Sensitivity and Specificity</subject><subject>support vector machine</subject><subject>support vector machines</subject><subject>Tissues</subject><subject>tumours</subject><issn>0094-2405</issn><issn>2473-4209</issn><issn>0094-2405</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kU1vFDEMhiMEotvCgT-AckRIU5yvJnNBQoUCUqtygHPkzWSWVDOTIckULb-erHap2kM52bIfv7b8EvKKwSljzLxjp1JzrpV4QlZcatFIDu1TsgJoZcMlqCNynPMNAJwJBc_JEedGgJZ8RW4-ehdyiBPNyzzHVGje5uJH2sdEh-hwCH_CtKFzirlg8dTh5Hyia8y-o3VsXIYSZkw4-pKCoyNuJl9qknyO0w6modaqxgvyrMch-5eHeEJ-XHz6fv6lubz-_PX8w2XjpGxFg84YROhkq91ZaxQyByjWvOdKc6PFGkzHDWfYIXbGKO286ZVxKDrfomrFCXm_152X9eg756eScLBzqnekrY0Y7MPOFH7aTby1QrQA0lSBNweBFH8tPhc7huz8MODk45ItAwGglRGyoq_v77pb8u_BFWj2wO8w-O1dn4HdOWeZPThnr77tQuXf7vnsQv13Nebxmf_BtzHdE5-7XvwFyR2pYA</recordid><startdate>201207</startdate><enddate>201207</enddate><creator>Shah, Vijay</creator><creator>Turkbey, Baris</creator><creator>Mani, Haresh</creator><creator>Pang, Yuxi</creator><creator>Pohida, Thomas</creator><creator>Merino, Maria J.</creator><creator>Pinto, Peter A.</creator><creator>Choyke, Peter L.</creator><creator>Bernardo, Marcelino</creator><general>American Association of Physicists in Medicine</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201207</creationdate><title>Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging</title><author>Shah, Vijay ; Turkbey, Baris ; Mani, Haresh ; Pang, Yuxi ; Pohida, Thomas ; Merino, Maria J. ; Pinto, Peter A. ; Choyke, Peter L. ; Bernardo, Marcelino</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4493-ac88aa0d497c6985a1c0a3b2f2572873b08d2821adaad8857ce8f58ca3de9a593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Aged</topic><topic>Algorithms</topic><topic>Biomedical modeling</topic><topic>biomedical MRI</topic><topic>Cancer</topic><topic>Clinical applications</topic><topic>Cluster analysis</topic><topic>Computer software</topic><topic>Computer systems utilizing knowledge based models</topic><topic>decision support system</topic><topic>decision support systems</topic><topic>Decision Support Systems, Clinical</topic><topic>Digital computing or data processing equipment or methods, specially adapted for specific applications</topic><topic>genetic algorithms</topic><topic>Humans</topic><topic>Image data processing or generation, in general</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>image segmentation</topic><topic>In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines</topic><topic>Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging</topic><topic>learning (artificial intelligence)</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Magnetic Resonance Physics</topic><topic>Male</topic><topic>medical computing</topic><topic>medical image processing</topic><topic>Medical image segmentation</topic><topic>Medical imaging</topic><topic>Medical magnetic resonance imaging</topic><topic>Middle Aged</topic><topic>multiparametric MRI</topic><topic>multispectral MRI</topic><topic>Pattern Recognition, Automated - methods</topic><topic>probability</topic><topic>prostate cancer localization</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Radiologists</topic><topic>Reproducibility of Results</topic><topic>Segmentation</topic><topic>Sensitivity and Specificity</topic><topic>support vector machine</topic><topic>support vector machines</topic><topic>Tissues</topic><topic>tumours</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shah, Vijay</creatorcontrib><creatorcontrib>Turkbey, Baris</creatorcontrib><creatorcontrib>Mani, Haresh</creatorcontrib><creatorcontrib>Pang, Yuxi</creatorcontrib><creatorcontrib>Pohida, Thomas</creatorcontrib><creatorcontrib>Merino, Maria J.</creatorcontrib><creatorcontrib>Pinto, Peter A.</creatorcontrib><creatorcontrib>Choyke, Peter L.</creatorcontrib><creatorcontrib>Bernardo, Marcelino</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shah, Vijay</au><au>Turkbey, Baris</au><au>Mani, Haresh</au><au>Pang, Yuxi</au><au>Pohida, Thomas</au><au>Merino, Maria J.</au><au>Pinto, Peter A.</au><au>Choyke, Peter L.</au><au>Bernardo, Marcelino</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2012-07</date><risdate>2012</risdate><volume>39</volume><issue>7</issue><spage>4093</spage><epage>4103</epage><pages>4093-4103</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><eissn>0094-2405</eissn><coden>MPHYA6</coden><abstract>Purpose:
There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).
Methods:
This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.
Results:
For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.
Conclusions:
This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accuratelylocalizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>22830742</pmid><doi>10.1118/1.4722753</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Algorithms Biomedical modeling biomedical MRI Cancer Clinical applications Cluster analysis Computer software Computer systems utilizing knowledge based models decision support system decision support systems Decision Support Systems, Clinical Digital computing or data processing equipment or methods, specially adapted for specific applications genetic algorithms Humans Image data processing or generation, in general Image Enhancement - methods Image Interpretation, Computer-Assisted - methods image segmentation In which a programme is changed according to experience gained by the computer itself during a complete run Learning machines Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging learning (artificial intelligence) Magnetic resonance imaging Magnetic Resonance Imaging - methods Magnetic Resonance Physics Male medical computing medical image processing Medical image segmentation Medical imaging Medical magnetic resonance imaging Middle Aged multiparametric MRI multispectral MRI Pattern Recognition, Automated - methods probability prostate cancer localization Prostatic Neoplasms - diagnosis Radiologists Reproducibility of Results Segmentation Sensitivity and Specificity support vector machine support vector machines Tissues tumours |
| title | Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging |
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