Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

Detalhes bibliográficos
Autor(a) principal: Farias, Erick Costa de
Data de Publicação: 2021
Outros Autores: Di Noia, Christian, Han, Changhee, Sala, Evis, Castelli, Mauro, Rundo, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/127078
Resumo: Farias, E. C. D., Di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021). Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Scientific Reports, 11(21361), 1-12. [21361]. https://doi.org/10.1038/s41598-021-00898-z -----------------------------------------This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. This work was partially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the Projects GADgET (DSAIPA/DS/0022/2018) and the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic featuresradiomic featuresClinica studiesGenerative Adversarial Network (GAN)Computed tomography (CT)GeneralSDG 3 - Good Health and Well-beingFarias, E. C. D., Di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021). Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Scientific Reports, 11(21361), 1-12. [21361]. https://doi.org/10.1038/s41598-021-00898-z -----------------------------------------This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. This work was partially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the Projects GADgET (DSAIPA/DS/0022/2018) and the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At 2× SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at 4× SR. We also evaluated the robustness of our model’s radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNFarias, Erick Costa deDi Noia, ChristianHan, ChangheeSala, EvisCastelli, MauroRundo, Leonardo2021-11-03T05:03:31Z2021-12-012021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/127078eng2045-2322PURE: 34619112https://doi.org/10.1038/s41598-021-00898-zinfo: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:RCAAP2024-03-11T05:07:11Zoai:run.unl.pt:10362/127078Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:01.966300Repositório 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 Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
title Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
spellingShingle Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
Farias, Erick Costa de
radiomic features
Clinica studies
Generative Adversarial Network (GAN)
Computed tomography (CT)
General
SDG 3 - Good Health and Well-being
title_short Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
title_full Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
title_fullStr Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
title_full_unstemmed Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
title_sort Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
author Farias, Erick Costa de
author_facet Farias, Erick Costa de
Di Noia, Christian
Han, Changhee
Sala, Evis
Castelli, Mauro
Rundo, Leonardo
author_role author
author2 Di Noia, Christian
Han, Changhee
Sala, Evis
Castelli, Mauro
Rundo, Leonardo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Farias, Erick Costa de
Di Noia, Christian
Han, Changhee
Sala, Evis
Castelli, Mauro
Rundo, Leonardo
dc.subject.por.fl_str_mv radiomic features
Clinica studies
Generative Adversarial Network (GAN)
Computed tomography (CT)
General
SDG 3 - Good Health and Well-being
topic radiomic features
Clinica studies
Generative Adversarial Network (GAN)
Computed tomography (CT)
General
SDG 3 - Good Health and Well-being
description Farias, E. C. D., Di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021). Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Scientific Reports, 11(21361), 1-12. [21361]. https://doi.org/10.1038/s41598-021-00898-z -----------------------------------------This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. This work was partially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the Projects GADgET (DSAIPA/DS/0022/2018) and the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2021
dc.date.none.fl_str_mv 2021-11-03T05:03:31Z
2021-12-01
2021-12-01T00:00:00Z
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 http://hdl.handle.net/10362/127078
url http://hdl.handle.net/10362/127078
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2045-2322
PURE: 34619112
https://doi.org/10.1038/s41598-021-00898-z
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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
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