Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Breast Radiation Therapy; Machine-Learning Prediction; Acute Desquamation

Detalhes bibliográficos
Autores: Aldraimli, Mahmoud, Osman, Sarah, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Gutierrez Enriquez, Sara, Reyes López, Victoria, Giraldo Marin, Alexandra
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2022
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:11351/8072
Acesso em linha:https://hdl.handle.net/11351/8072
http://hdl.handle.net/11351/8072
Access Level:Acceso aberto
Palavra-chave:Mama - Càncer - Radioteràpia
Pell - Efecte de la radiació
Aprenentatge automàtic
ANATOMY::Integumentary System::Skin
Other subheadings::Other subheadings::/radiation effects
DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms
Other subheadings::Other subheadings::Other subheadings::/radiotherapy
ANATOMÍA::integumento común::piel
Otros calificadores::Otros calificadores::/efectos de la radiación
ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama
Otros calificadores::Otros calificadores::Otros calificadores::/radioterapia
id ES_6d3bc0376cbcf7148625efeaf2079e38
oai_identifier_str oai:recercat.cat:11351/8072
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
title Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
spellingShingle Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
Aldraimli, Mahmoud
Mama - Càncer - Radioteràpia
Pell - Efecte de la radiació
Aprenentatge automàtic
ANATOMY::Integumentary System::Skin
Other subheadings::Other subheadings::/radiation effects
DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms
Other subheadings::Other subheadings::Other subheadings::/radiotherapy
ANATOMÍA::integumento común::piel
Otros calificadores::Otros calificadores::/efectos de la radiación
ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama
Otros calificadores::Otros calificadores::Otros calificadores::/radioterapia
title_short Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
title_full Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
title_fullStr Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
title_full_unstemmed Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
title_sort Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
dc.creator.none.fl_str_mv Aldraimli, Mahmoud
Osman, Sarah
Grishchuck, Diana
Ingram, Samuel
Lyon, Robert
Mistry, Anil
Gutierrez Enriquez, Sara
Reyes López, Victoria
Giraldo Marin, Alexandra
author Aldraimli, Mahmoud
author_facet Aldraimli, Mahmoud
Osman, Sarah
Grishchuck, Diana
Ingram, Samuel
Lyon, Robert
Mistry, Anil
Gutierrez Enriquez, Sara
Reyes López, Victoria
Giraldo Marin, Alexandra
author_role author
author2 Osman, Sarah
Grishchuck, Diana
Ingram, Samuel
Lyon, Robert
Mistry, Anil
Gutierrez Enriquez, Sara
Reyes López, Victoria
Giraldo Marin, Alexandra
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Institut Català de la Salut
[Aldraimli M] Health Innovation Ecosystem, University of Westminster, London, United Kingdom. [Osman S] Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom. [Grishchuck D] Imperial College Healthcare NHS Trust, London, United Kingdom. [Ingram S] Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom. [Lyon R] Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom. [Mistry A] Guy's and St. Thomas’ NHS Foundation Trust, London, United Kingdom. [Giraldo A, Reyes V] Servei d’Oncologia Radioteràpica, Vall d'Hebron Hospital Universitari, Barcelona, Spain. [Gutiérrez-Enríquez S] Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
dc.subject.none.fl_str_mv Mama - Càncer - Radioteràpia
Pell - Efecte de la radiació
Aprenentatge automàtic
ANATOMY::Integumentary System::Skin
Other subheadings::Other subheadings::/radiation effects
DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms
Other subheadings::Other subheadings::Other subheadings::/radiotherapy
ANATOMÍA::integumento común::piel
Otros calificadores::Otros calificadores::/efectos de la radiación
ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama
Otros calificadores::Otros calificadores::Otros calificadores::/radioterapia
topic Mama - Càncer - Radioteràpia
Pell - Efecte de la radiació
Aprenentatge automàtic
ANATOMY::Integumentary System::Skin
Other subheadings::Other subheadings::/radiation effects
DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms
Other subheadings::Other subheadings::Other subheadings::/radiotherapy
ANATOMÍA::integumento común::piel
Otros calificadores::Otros calificadores::/efectos de la radiación
ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama
Otros calificadores::Otros calificadores::Otros calificadores::/radioterapia
description Breast Radiation Therapy; Machine-Learning Prediction; Acute Desquamation
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11351/8072
http://hdl.handle.net/11351/8072
url https://hdl.handle.net/11351/8072
http://hdl.handle.net/11351/8072
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Advances in Radiation Oncology;7(3)
https://doi.org/10.1016/j.adro.2021.100890
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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 Scientia
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410337259782144
spelling Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE CohortAldraimli, MahmoudOsman, SarahGrishchuck, DianaIngram, SamuelLyon, RobertMistry, AnilGutierrez Enriquez, SaraReyes López, VictoriaGiraldo Marin, AlexandraMama - Càncer - RadioteràpiaPell - Efecte de la radiacióAprenentatge automàticANATOMY::Integumentary System::SkinOther subheadings::Other subheadings::/radiation effectsDISEASES::Neoplasms::Neoplasms by Site::Breast NeoplasmsOther subheadings::Other subheadings::Other subheadings::/radiotherapyANATOMÍA::integumento común::pielOtros calificadores::Otros calificadores::/efectos de la radiaciónENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mamaOtros calificadores::Otros calificadores::Otros calificadores::/radioterapiaBreast Radiation Therapy; Machine-Learning Prediction; Acute DesquamationRaditeràpia de mama; Predicció d'aprenentatge automàtic; Descamació agudaRadioterapia de mama; Predicción de aprendizaje automático; Descamación agudaPurpose Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.ElsevierInstitut Català de la Salut[Aldraimli M] Health Innovation Ecosystem, University of Westminster, London, United Kingdom. [Osman S] Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom. [Grishchuck D] Imperial College Healthcare NHS Trust, London, United Kingdom. [Ingram S] Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom. [Lyon R] Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom. [Mistry A] Guy's and St. Thomas’ NHS Foundation Trust, London, United Kingdom. [Giraldo A, Reyes V] Servei d’Oncologia Radioteràpica, Vall d'Hebron Hospital Universitari, Barcelona, Spain. [Gutiérrez-Enríquez S] Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, SpainVall d'Hebron Barcelona Hospital Campus202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/11351/8072http://hdl.handle.net/11351/8072Scientiareponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAdvances in Radiation Oncology;7(3)https://doi.org/10.1016/j.adro.2021.100890Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:11351/80722026-05-29T05:05:01Z
score 15,812455

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