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dc.creatorRuiz-Sarrias, O. (Oskitz)-
dc.creatorGónzalez-Deza, C. (Cristina)-
dc.creatorRodríguez-Rodríguez, J. (Javier)-
dc.creatorArrizibita-Iriarte, O. (Olast)-
dc.creatorVizcay-Atienza, A. (Angel)-
dc.creatorZumárraga-Lizundia, T. (Teresa)-
dc.creatorSayar, O. (Onintza)-
dc.creatorAldaz, A. (Azucena)-
dc.date.accessioned2024-01-17T12:58:23Z-
dc.date.available2024-01-17T12:58:23Z-
dc.date.issued2023-
dc.identifier.citationRuiz-Sarrias, O. (Oskitz); Gónzalez-Deza, C. (Cristina); Rodríguez-Rodríguez, J. (Javier); et al. "Predicting severe haematological toxicity in gastrointestinal cancer patients undergoing 5-FU-based chemotherapy: A bayesian network approach". Cancers. 15 (17), 2023, 4206es_ES
dc.identifier.issn2072-6694-
dc.identifier.urihttps://hdl.handle.net/10171/68406-
dc.description.abstractPurpose: Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. Methods: We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model’s performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. Results: The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. Conclusions: Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.es_ES
dc.description.sponsorshipThis research is a part of the REINFORCE project, strategically supported by the Government of Navarra’s Department of Economic and Business Development. Reference: 0011-1411-2020- 000074. From 22 June 2020 to 30 November 2022. Funding GOBIERNO DE NAVARRA, Convocatoria: 2020 GN PROYECTOS ESTRATEGICOS DE I+D 2020–2022.es_ES
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectHaematotoxicity predictiones_ES
dc.subject5-FUes_ES
dc.subjectNeutropeniaes_ES
dc.subjectThrombocytopeniaes_ES
dc.subjectLeukopeniaes_ES
dc.subjectGastrointestinal canceres_ES
dc.subjectBayesian networkes_ES
dc.subjectMachine learninges_ES
dc.subjectArtificial intelligencees_ES
dc.titlePredicting severe haematological toxicity in gastrointestinal cancer patients undergoing 5-FU-based chemotherapy: A bayesian network approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.noteThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).es_ES
dc.identifier.doi10.3390/cancers15174206-
dadun.citation.number17es_ES
dadun.citation.publicationNameCancerses_ES
dadun.citation.startingPage4206es_ES
dadun.citation.volume15es_ES
dc.identifier.pmid37686482-

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