Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

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dc.authoridUyulan, Caglar/0000-0002-6423-6720
dc.authoriduslu, rabiye/0000-0002-5542-7453
dc.authoridAYDUR PUREN, Busra/0000-0002-5586-2738
dc.authoridTekin, Huseyin Ozan/0000-0002-0997-3488
dc.authoridOZDEMIR, Semra/0000-0003-1302-9630
dc.authoridErguzel, Turker/0000-0001-8438-6542
dc.authoridSalihoglu, Yavuz Sami/0000-0003-2465-9128
dc.contributor.authorSalihoglu, Yavuz Sami
dc.contributor.authorErdemir, Rabiye Uslu
dc.contributor.authorPuren, Busra Aydur
dc.contributor.authorOzdemir, Semra
dc.contributor.authorUyulan, Caglar
dc.contributor.authorErguzel, Turker Tekin
dc.contributor.authorTekin, Huseyin Ozan
dc.date.accessioned2025-01-27T20:43:44Z
dc.date.available2025-01-27T20:43:44Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractObjectives: This study aimed to evaluate the ability of (18)fluorine-fluorodeoxyglucose (F-18-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). Methods: Data of 48 patients with SPN detected on F-18-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC). Results: The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs similar to 0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). Conclusion: Machine learning based on F-18-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.
dc.identifier.doi10.4274/mirt.galenos.2021.43760
dc.identifier.endpage88
dc.identifier.issn2146-1414
dc.identifier.issn2147-1959
dc.identifier.issue2
dc.identifier.pmid35770958
dc.identifier.scopus2-s2.0-85134081221
dc.identifier.scopusqualityQ3
dc.identifier.startpage82
dc.identifier.trdizinid535014
dc.identifier.urihttps://doi.org/10.4274/mirt.galenos.2021.43760
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/535014
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24353
dc.identifier.volume31
dc.identifier.wosWOS:000823262600002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherGalenos Publ House
dc.relation.ispartofMolecular Imaging and Radionuclide Therapy
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectSolitary pulmonary nodule
dc.subjectPET/CT
dc.subjectradiomic
dc.subjectmachine learning
dc.titleDiagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules
dc.title.alternativeSoliter Pulmoner Nodüllerin Sınıflandırılmasında18F-FDG PET/BT Radyomik Özelliklerine Dayalı Makine Öğrenme Modellerinin Tanısal Performansı
dc.typeArticle

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