Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/108105
DC Field | Value | Language |
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dc.contributor.author | Nogueira, Mariana A. | - |
dc.contributor.author | Abreu, Pedro H. | - |
dc.contributor.author | Martins, Pedro | - |
dc.contributor.author | Machado, Penousal | - |
dc.contributor.author | Duarte, Hugo | - |
dc.contributor.author | Santos, João | - |
dc.date.accessioned | 2023-08-11T15:08:12Z | - |
dc.date.available | 2023-08-11T15:08:12Z | - |
dc.date.issued | 2017-02-13 | - |
dc.identifier.issn | 1471-2342 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/108105 | - |
dc.description.abstract | Background: Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | project NORTE-01-0145-FEDER-000027, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Artificial neural networks | pt |
dc.subject | Images descriptors | pt |
dc.subject | PET/CT images | pt |
dc.subject | Treatment response assessment | pt |
dc.subject.mesh | Algorithms | pt |
dc.subject.mesh | Female | pt |
dc.subject.mesh | Hodgkin Disease | pt |
dc.subject.mesh | Humans | pt |
dc.subject.mesh | Male | pt |
dc.subject.mesh | Neural Networks, Computer | pt |
dc.subject.mesh | Neuroendocrine Tumors | pt |
dc.subject.mesh | Pattern Recognition, Automated | pt |
dc.subject.mesh | Positron Emission Tomography Computed Tomography | pt |
dc.subject.mesh | Reproducibility of Results | pt |
dc.subject.mesh | Treatment Outcome | pt |
dc.subject.mesh | Whole Body Imaging | pt |
dc.title | An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images | pt |
dc.type | article | - |
degois.publication.firstPage | 13 | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | BMC Medical Imaging | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1186/s12880-017-0181-0 | pt |
degois.publication.volume | 17 | pt |
dc.date.embargo | 2017-02-13 | * |
uc.date.periodoEmbargo | 0 | pt |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | Com Texto completo | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0002-1578-0594 | - |
crisitem.author.orcid | 0000-0001-6071-4038 | - |
crisitem.author.orcid | 0000-0002-6308-6484 | - |
Appears in Collections: | FCTUC Eng.Informática - Artigos em Revistas Internacionais I&D CISUC - Artigos em Revistas Internacionais |
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An-artificial-neural-networks-approach-for-assessment-treatment-response-in-oncological-patients-using-PETCT-imagesBMC-Medical-Imaging.pdf | 477.44 kB | Adobe PDF | View/Open |
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