Selection of Radiomics Features based on their Reproducibility

Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact o...

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Autores: Ligero, Marta, Torres, Guillermo|||0000-0002-1576-6178, Sánchez Ramos, Carles|||0000-0003-3435-9882, Diaz-Chito, Katerine|||0000-0002-8860-8082, Pérez, Raquel, Gil, Debora|||0000-0002-2770-4767
Tipo de recurso: capítulo de libro
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:257861
Acceso en línea:https://ddd.uab.cat/record/257861
https://dx.doi.org/urn:doi:10.1109/EMBC.2019.8857879
Access Level:acceso abierto
Palabra clave:Feature Selection
Reproducibility
Radiomics
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spelling Selection of Radiomics Features based on their ReproducibilityLigero, MartaTorres, Guillermo|||0000-0002-1576-6178Sánchez Ramos, Carles|||0000-0003-3435-9882Diaz-Chito, Katerine|||0000-0002-8860-8082Pérez, RaquelGil, Debora|||0000-0002-2770-4767Feature SelectionReproducibilityRadiomicsDimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.Institute of Electrical and Electronics Engineers (IEEE) 22019-01-0120192019-01-01Capítol de llibrehttp://purl.org/coar/resource_type/c_3248AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/bookPartapplication/pdfhttps://ddd.uab.cat/record/257861https://dx.doi.org/urn:doi:10.1109/EMBC.2019.8857879reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 712949Ministerio de Economía, Industria y Competitividad https://doi.org/10.13039/501100010198 FIS-G64384969Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-1624open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2578612026-06-06T12:50:31Z
dc.title.none.fl_str_mv Selection of Radiomics Features based on their Reproducibility
title Selection of Radiomics Features based on their Reproducibility
spellingShingle Selection of Radiomics Features based on their Reproducibility
Ligero, Marta
Feature Selection
Reproducibility
Radiomics
title_short Selection of Radiomics Features based on their Reproducibility
title_full Selection of Radiomics Features based on their Reproducibility
title_fullStr Selection of Radiomics Features based on their Reproducibility
title_full_unstemmed Selection of Radiomics Features based on their Reproducibility
title_sort Selection of Radiomics Features based on their Reproducibility
dc.creator.none.fl_str_mv Ligero, Marta
Torres, Guillermo|||0000-0002-1576-6178
Sánchez Ramos, Carles|||0000-0003-3435-9882
Diaz-Chito, Katerine|||0000-0002-8860-8082
Pérez, Raquel
Gil, Debora|||0000-0002-2770-4767
author Ligero, Marta
author_facet Ligero, Marta
Torres, Guillermo|||0000-0002-1576-6178
Sánchez Ramos, Carles|||0000-0003-3435-9882
Diaz-Chito, Katerine|||0000-0002-8860-8082
Pérez, Raquel
Gil, Debora|||0000-0002-2770-4767
author_role author
author2 Torres, Guillermo|||0000-0002-1576-6178
Sánchez Ramos, Carles|||0000-0003-3435-9882
Diaz-Chito, Katerine|||0000-0002-8860-8082
Pérez, Raquel
Gil, Debora|||0000-0002-2770-4767
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Feature Selection
Reproducibility
Radiomics
topic Feature Selection
Reproducibility
Radiomics
description Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.
publishDate 2019
dc.date.none.fl_str_mv 2
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv Capítol de llibre
http://purl.org/coar/resource_type/c_3248
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/257861
https://dx.doi.org/urn:doi:10.1109/EMBC.2019.8857879
url https://ddd.uab.cat/record/257861
https://dx.doi.org/urn:doi:10.1109/EMBC.2019.8857879
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 712949
Ministerio de Economía, Industria y Competitividad https://doi.org/10.13039/501100010198 FIS-G64384969
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-1624
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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