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dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorAldraimli, Mahmoud
dc.contributor.authorOsman, Sarah
dc.contributor.authorGrishchuck, Diana
dc.contributor.authorIngram, Samuel
dc.contributor.authorLyon, Robert
dc.contributor.authorMistry, Anil
dc.contributor.authorGiraldo Marin, Alexandra
dc.contributor.authorGutierrez Enriquez, Sara
dc.contributor.authorReyes López, Victoria
dc.date.accessioned2022-09-08T11:26:08Z
dc.date.available2022-09-08T11:26:08Z
dc.date.issued2022-05
dc.identifier.citationAldraimli M, Osman S, Grishchuck D, Ingram S, Lyon R, Mistry A, et al. Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort. Adv Radiat Oncol. 2022 May;7(3):100890.
dc.identifier.issn2452-1094
dc.identifier.urihttp://hdl.handle.net/11351/8072
dc.descriptionBreast Radiation Therapy; Machine-Learning Prediction; Acute Desquamation
dc.description.abstractPurpose 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.
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesAdvances in Radiation Oncology;7(3)
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScientia
dc.subjectMama - Càncer - Radioteràpia
dc.subjectPell - Efecte de la radiació
dc.subjectAprenentatge automàtic
dc.subject.meshSkin
dc.subject.mesh/radiation effects
dc.subject.meshBreast Neoplasms
dc.subject.mesh/radiotherapy
dc.titleDevelopment and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.adro.2021.100890
dc.subject.decspiel
dc.subject.decs/efectos de la radiación
dc.subject.decsneoplasias de la mama
dc.subject.decs/radioterapia
dc.relation.publishversionhttps://doi.org/10.1016/j.adro.2021.100890
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[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
dc.identifier.pmid35647396
dc.identifier.wos000832948800013
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


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