Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks
The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in...
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| Veröffentlicht in: | Breast cancer research and treatment 2005-12, Vol.94 (3), p.265-272 |
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| creator | JEREZ, J. M FRANCE, L ALBA, E LLOMBART-CUSSAC, A LLUCH, A RIBELLES, N MUNARRIZ, B MARTIN, M |
| description | The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset. |
| doi_str_mv | 10.1007/s10549-005-9013-y |
| format | Article |
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M ; FRANCE, L ; ALBA, E ; LLOMBART-CUSSAC, A ; LLUCH, A ; RIBELLES, N ; MUNARRIZ, B ; MARTIN, M</creator><creatorcontrib>JEREZ, J. M ; FRANCE, L ; ALBA, E ; LLOMBART-CUSSAC, A ; LLUCH, A ; RIBELLES, N ; MUNARRIZ, B ; MARTIN, M</creatorcontrib><description>The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.</description><identifier>ISSN: 0167-6806</identifier><identifier>EISSN: 1573-7217</identifier><identifier>DOI: 10.1007/s10549-005-9013-y</identifier><identifier>PMID: 16254686</identifier><identifier>CODEN: BCTRD6</identifier><language>eng</language><publisher>Dordrecht: Springer</publisher><subject>Adult ; Age Factors ; Aged ; Aged, 80 and over ; Biological and medical sciences ; Breast cancer ; Breast Neoplasms - pathology ; Cancer research ; Cancer therapies ; Comparative analysis ; Female ; Forecasting ; Gynecology. Andrology. Obstetrics ; Humans ; Lymphatic Metastasis ; Mammary gland diseases ; Medical sciences ; Middle Aged ; Neoplasm Staging ; Neural networks ; Neural Networks (Computer) ; Predictions ; Prognosis ; Proportional Hazards Models ; Risk Assessment ; Risk factors ; Survival analysis ; Tumors</subject><ispartof>Breast cancer research and treatment, 2005-12, Vol.94 (3), p.265-272</ispartof><rights>2006 INIST-CNRS</rights><rights>Springer Science+Business Media, Inc. 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-9649cddfb0819c7f7062513a4e0260d8e265f971515cdd36df2f221c79ddf2be3</citedby><cites>FETCH-LOGICAL-c356t-9649cddfb0819c7f7062513a4e0260d8e265f971515cdd36df2f221c79ddf2be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17345866$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16254686$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>JEREZ, J. M</creatorcontrib><creatorcontrib>FRANCE, L</creatorcontrib><creatorcontrib>ALBA, E</creatorcontrib><creatorcontrib>LLOMBART-CUSSAC, A</creatorcontrib><creatorcontrib>LLUCH, A</creatorcontrib><creatorcontrib>RIBELLES, N</creatorcontrib><creatorcontrib>MUNARRIZ, B</creatorcontrib><creatorcontrib>MARTIN, M</creatorcontrib><title>Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks</title><title>Breast cancer research and treatment</title><addtitle>Breast Cancer Res Treat</addtitle><description>The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.</description><subject>Adult</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biological and medical sciences</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - pathology</subject><subject>Cancer research</subject><subject>Cancer therapies</subject><subject>Comparative analysis</subject><subject>Female</subject><subject>Forecasting</subject><subject>Gynecology. Andrology. Obstetrics</subject><subject>Humans</subject><subject>Lymphatic Metastasis</subject><subject>Mammary gland diseases</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Neoplasm Staging</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Predictions</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Risk Assessment</subject><subject>Risk factors</subject><subject>Survival analysis</subject><subject>Tumors</subject><issn>0167-6806</issn><issn>1573-7217</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkUtrHDEQhEVIiNdOfkAuQQTi2yTd0kgaHYPJw2DIJTkLraa1lj2PjTRjs_8-cnbBkFPR8FVT3cXYO4RPCGA-FwTV2gZANRZQNocXbIPKyMYINC_ZBlCbRnegz9h5KXcAYA3Y1-wMtVCt7vSG7a7HfZ4faKRp4XPk20y-LDz4KVDmmQa_L8T3mfoUljRPPE38Nu1ueU7lvg4L5Qc_FL6WNO24z0uKKSQ_8InW_E-WxznflzfsVawcvT3pBfv97euvqx_Nzc_v11dfbpoglV4aq1sb-j5uoUMbTDRQk6L0LYHQ0HcktIrWoEJVMan7KKIQGIytJrElecEuj3vrVX9WKosbUwk0DH6ieS1Od51S1uoKfvgPvJvXPNVsTqBoNVopKoRHKOS5lEzR7XMafT44BPdUgTtW4GoF7qkCd6ie96fF63ak_tlx-nkFPp4AX4IfYq6_TuWZM7JVndbyL8ZckC8</recordid><startdate>20051201</startdate><enddate>20051201</enddate><creator>JEREZ, J. 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M ; FRANCE, L ; ALBA, E ; LLOMBART-CUSSAC, A ; LLUCH, A ; RIBELLES, N ; MUNARRIZ, B ; MARTIN, M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-9649cddfb0819c7f7062513a4e0260d8e265f971515cdd36df2f221c79ddf2be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Adult</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biological and medical sciences</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - pathology</topic><topic>Cancer research</topic><topic>Cancer therapies</topic><topic>Comparative analysis</topic><topic>Female</topic><topic>Forecasting</topic><topic>Gynecology. Andrology. Obstetrics</topic><topic>Humans</topic><topic>Lymphatic Metastasis</topic><topic>Mammary gland diseases</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Neoplasm Staging</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Predictions</topic><topic>Prognosis</topic><topic>Proportional Hazards Models</topic><topic>Risk Assessment</topic><topic>Risk factors</topic><topic>Survival analysis</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>JEREZ, J. 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M</au><au>FRANCE, L</au><au>ALBA, E</au><au>LLOMBART-CUSSAC, A</au><au>LLUCH, A</au><au>RIBELLES, N</au><au>MUNARRIZ, B</au><au>MARTIN, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks</atitle><jtitle>Breast cancer research and treatment</jtitle><addtitle>Breast Cancer Res Treat</addtitle><date>2005-12-01</date><risdate>2005</risdate><volume>94</volume><issue>3</issue><spage>265</spage><epage>272</epage><pages>265-272</pages><issn>0167-6806</issn><eissn>1573-7217</eissn><coden>BCTRD6</coden><abstract>The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.</abstract><cop>Dordrecht</cop><pub>Springer</pub><pmid>16254686</pmid><doi>10.1007/s10549-005-9013-y</doi><tpages>8</tpages></addata></record> |
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| ispartof | Breast cancer research and treatment, 2005-12, Vol.94 (3), p.265-272 |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Adult Age Factors Aged Aged, 80 and over Biological and medical sciences Breast cancer Breast Neoplasms - pathology Cancer research Cancer therapies Comparative analysis Female Forecasting Gynecology. Andrology. Obstetrics Humans Lymphatic Metastasis Mammary gland diseases Medical sciences Middle Aged Neoplasm Staging Neural networks Neural Networks (Computer) Predictions Prognosis Proportional Hazards Models Risk Assessment Risk factors Survival analysis Tumors |
| title | Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks |
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