Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks

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dc.authoridAltinok, Oya/0000-0002-8713-0697
dc.authoridGuvenis, Albert/0000-0003-0490-5184
dc.contributor.authorAltinok, Oya
dc.contributor.authorGuvenis, Albert
dc.date.accessioned2024-10-29T17:58:24Z
dc.date.available2024-10-29T17:58:24Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractObjectives: To develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. Methods: Two hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients' contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. Results: The Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data.Conclusions: The straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. Advances in Knowledge: Radiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.
dc.identifier.doi10.1016/j.ejmp.2023.102671
dc.identifier.issn1120-1797
dc.identifier.issn1724-191X
dc.identifier.pmid37708571
dc.identifier.scopus2-s2.0-85171387776
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ejmp.2023.102671
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14289
dc.identifier.volume114
dc.identifier.wosWOS:001075957700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofPhysica Medica-European Journal of Medical Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBayesian Networks
dc.subjectHPV
dc.subjectOropharyngeal Cancer
dc.titleInterpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks
dc.typeArticle

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