Radiomics in head and neck cancer outcome predictions

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Maria
Data de Publicação: 2022
Outros Autores: Gsaxner, Christina, Ferreira, André, Li, Jianning, Puladi, Behrus, Kleesiek, Jens, Egger, Jan, Alves, Victor
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/81081
Resumo: The data are publicly available on The Cancer Image Archive (TCIA) [41] website and can be downloaded using the NBIA Data Retriever [42]: https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-PET-CT, accessed on 11 October 2022. The source code is available on GitHub: https://github.com/MariaGoncalves3/Radiomics_for_Head_And_Neck_Cancer, accessed on 11 October 2022.
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spelling Radiomics in head and neck cancer outcome predictionsPrecision medicineHead and neck cancerRadiomicsLocoregional recurrencesDistant metastasesOverall survivalCTMultilayer perceptronXGBoostScience & TechnologyThe data are publicly available on The Cancer Image Archive (TCIA) [41] website and can be downloaded using the NBIA Data Retriever [42]: https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-PET-CT, accessed on 11 October 2022. The source code is available on GitHub: https://github.com/MariaGoncalves3/Radiomics_for_Head_And_Neck_Cancer, accessed on 11 October 2022.Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients’ clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.This work received funding from the Austrian Science Fund (FWF) KLI 678-B31: “enFaced-Virtual and Augmented Reality Training and Navigation Module for 3D-Printed Facial Defect Reconstructions”,FWF KLI 1044: “enFaced 2.0-Instant AR Tool for Maxillofacial Surgery” (https://enfaced2.ikim.nrw/, accessed on 11 October 2022), “CAMed” (COMET K-Project 871132), which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), the Styrian Business Promotion Agency (SFG), and the FCT-Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. Further, we acknowledge the REACT-EU project KITE (Plattform für KI-Translation Essen, https://kite.ikim.nrw/, accessed on 11 October 2022).Multidisciplinary Digital Publishing InstituteUniversidade do MinhoGonçalves, MariaGsaxner, ChristinaFerreira, AndréLi, JianningPuladi, BehrusKleesiek, JensEgger, JanAlves, Victor2022-11-082022-11-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/81081engGonçalves, M.; Gsaxner, C.; Ferreira, A.; Li, J.; Puladi, B.; Kleesiek, J.; Egger, J.; Alves, V. Radiomics in Head and Neck Cancer Outcome Predictions. Diagnostics 2022, 12, 2733. https://doi.org/10.3390/diagnostics121127332075-441810.3390/diagnostics12112733https://www.mdpi.com/2075-4418/12/11/2733info: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:RCAAP2023-07-21T12:52:21Zoai:repositorium.sdum.uminho.pt:1822/81081Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:51:26.307085Repositório 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 Radiomics in head and neck cancer outcome predictions
title Radiomics in head and neck cancer outcome predictions
spellingShingle Radiomics in head and neck cancer outcome predictions
Gonçalves, Maria
Precision medicine
Head and neck cancer
Radiomics
Locoregional recurrences
Distant metastases
Overall survival
CT
Multilayer perceptron
XGBoost
Science & Technology
title_short Radiomics in head and neck cancer outcome predictions
title_full Radiomics in head and neck cancer outcome predictions
title_fullStr Radiomics in head and neck cancer outcome predictions
title_full_unstemmed Radiomics in head and neck cancer outcome predictions
title_sort Radiomics in head and neck cancer outcome predictions
author Gonçalves, Maria
author_facet Gonçalves, Maria
Gsaxner, Christina
Ferreira, André
Li, Jianning
Puladi, Behrus
Kleesiek, Jens
Egger, Jan
Alves, Victor
author_role author
author2 Gsaxner, Christina
Ferreira, André
Li, Jianning
Puladi, Behrus
Kleesiek, Jens
Egger, Jan
Alves, Victor
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Gonçalves, Maria
Gsaxner, Christina
Ferreira, André
Li, Jianning
Puladi, Behrus
Kleesiek, Jens
Egger, Jan
Alves, Victor
dc.subject.por.fl_str_mv Precision medicine
Head and neck cancer
Radiomics
Locoregional recurrences
Distant metastases
Overall survival
CT
Multilayer perceptron
XGBoost
Science & Technology
topic Precision medicine
Head and neck cancer
Radiomics
Locoregional recurrences
Distant metastases
Overall survival
CT
Multilayer perceptron
XGBoost
Science & Technology
description The data are publicly available on The Cancer Image Archive (TCIA) [41] website and can be downloaded using the NBIA Data Retriever [42]: https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-PET-CT, accessed on 11 October 2022. The source code is available on GitHub: https://github.com/MariaGoncalves3/Radiomics_for_Head_And_Neck_Cancer, accessed on 11 October 2022.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-08
2022-11-08T00:00:00Z
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 https://hdl.handle.net/1822/81081
url https://hdl.handle.net/1822/81081
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Gonçalves, M.; Gsaxner, C.; Ferreira, A.; Li, J.; Puladi, B.; Kleesiek, J.; Egger, J.; Alves, V. Radiomics in Head and Neck Cancer Outcome Predictions. Diagnostics 2022, 12, 2733. https://doi.org/10.3390/diagnostics12112733
2075-4418
10.3390/diagnostics12112733
https://www.mdpi.com/2075-4418/12/11/2733
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 Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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