Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/14207 |
Resumo: | Aim To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. Materials and methods Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. Results ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10−3 mm2/s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10−3 mm2/s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). Conclusion The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance. |
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Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptorsBreast NeoplasmsContrast MediaDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleImage EnhancementImage Interpretation, Computer-AssistedMeglumineMiddle AgedOrganometallic CompoundsPredictive Value of TestsProspective StudiesAim To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. Materials and methods Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. Results ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10−3 mm2/s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10−3 mm2/s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). Conclusion The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance.ElsevierRepositório Científico do Instituto Politécnico do PortoNogueira, LuisaBrandão, SofiaMatos, EduardaGouveia Nunes, RitaFerreira, Hugo AlexandreLoureiro, JoanaRamos, Isabel2019-07-01T16:07:39Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/14207eng10.1016/j.crad.2015.05.009info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:56:55Zoai:recipp.ipp.pt:10400.22/14207Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:34:02.011124Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
title |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
spellingShingle |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors Nogueira, Luisa Breast Neoplasms Contrast Media Diagnosis, Differential Diffusion Magnetic Resonance Imaging Female Image Enhancement Image Interpretation, Computer-Assisted Meglumine Middle Aged Organometallic Compounds Predictive Value of Tests Prospective Studies |
title_short |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
title_full |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
title_fullStr |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
title_full_unstemmed |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
title_sort |
Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors |
author |
Nogueira, Luisa |
author_facet |
Nogueira, Luisa Brandão, Sofia Matos, Eduarda Gouveia Nunes, Rita Ferreira, Hugo Alexandre Loureiro, Joana Ramos, Isabel |
author_role |
author |
author2 |
Brandão, Sofia Matos, Eduarda Gouveia Nunes, Rita Ferreira, Hugo Alexandre Loureiro, Joana Ramos, Isabel |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Nogueira, Luisa Brandão, Sofia Matos, Eduarda Gouveia Nunes, Rita Ferreira, Hugo Alexandre Loureiro, Joana Ramos, Isabel |
dc.subject.por.fl_str_mv |
Breast Neoplasms Contrast Media Diagnosis, Differential Diffusion Magnetic Resonance Imaging Female Image Enhancement Image Interpretation, Computer-Assisted Meglumine Middle Aged Organometallic Compounds Predictive Value of Tests Prospective Studies |
topic |
Breast Neoplasms Contrast Media Diagnosis, Differential Diffusion Magnetic Resonance Imaging Female Image Enhancement Image Interpretation, Computer-Assisted Meglumine Middle Aged Organometallic Compounds Predictive Value of Tests Prospective Studies |
description |
Aim To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. Materials and methods Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. Results ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10−3 mm2/s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10−3 mm2/s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). Conclusion The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2019-07-01T16:07:39Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/14207 |
url |
http://hdl.handle.net/10400.22/14207 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.crad.2015.05.009 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131432273051648 |