Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/107516
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Patrício, Miguel | - |
dc.contributor.author | Pereira, José | - |
dc.contributor.author | Crisóstomo, Joana | - |
dc.contributor.author | Matafome, Paulo N. | - |
dc.contributor.author | Gomes, Manuel | - |
dc.contributor.author | Seiça, Raquel | - |
dc.contributor.author | Caramelo, Francisco | - |
dc.date.accessioned | 2023-07-18T09:43:08Z | - |
dc.date.available | 2023-07-18T09:43:08Z | - |
dc.date.issued | 2018-01-04 | - |
dc.identifier.issn | 1471-2407 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/107516 | - |
dc.description.abstract | Background: The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis. Methods: For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models. Results: Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91]. Conclusions: These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | UID/NEU/04539/2013 | pt |
dc.relation | PTDC/SAU-MET/121133/2010 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Breast cancer | pt |
dc.subject | Glucose | pt |
dc.subject | Resistin | pt |
dc.subject | BMI | pt |
dc.subject | Age | pt |
dc.subject | Biomarker | pt |
dc.subject.mesh | Aged | pt |
dc.subject.mesh | Blood Glucose | pt |
dc.subject.mesh | Body Mass Index | pt |
dc.subject.mesh | Breast Neoplasms | pt |
dc.subject.mesh | Female | pt |
dc.subject.mesh | Genetic Testing | pt |
dc.subject.mesh | Humans | pt |
dc.subject.mesh | Insulin | pt |
dc.subject.mesh | Insulin Resistance | pt |
dc.subject.mesh | Middle Aged | pt |
dc.subject.mesh | Obesity | pt |
dc.subject.mesh | Resistin | pt |
dc.title | Using Resistin, glucose, age and BMI to predict the presence of breast cancer | pt |
dc.type | article | - |
degois.publication.firstPage | 29 | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | BMC Cancer | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1186/s12885-017-3877-1 | pt |
degois.publication.volume | 18 | pt |
dc.date.embargo | 2018-01-04 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
item.fulltext | Com Texto completo | - |
item.cerifentitytype | Publications | - |
crisitem.author.researchunit | CNC - Center for Neuroscience and Cell Biology | - |
crisitem.author.researchunit | ICBR Coimbra Institute for Clinical and Biomedical Research | - |
crisitem.author.parentresearchunit | Faculty of Medicine | - |
crisitem.author.orcid | 0000-0002-3422-290X | - |
crisitem.author.orcid | 0000-0002-0015-8604 | - |
Appears in Collections: | I&D IBILI - Artigos em Revistas Internacionais FMUC Medicina - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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Using-Resistin-glucose-age-and-BMI-to-predict-the-presence-of-breast-cancerBMC-Cancer.pdf | 609.24 kB | Adobe PDF | View/Open |
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