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

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SALİHOĞLU Y. S., Erdemir R. U., Puren B. A., ÖZDEMİR S., Uyulan C., ERGÜZEL T. T., ...More

MOLECULAR IMAGING AND RADIONUCLIDE THERAPY, vol.31, no.2, pp.82-88, 2022 (ESCI, Scopus, TRDizin)

• Publication Type: Article / Article
• Volume: 31 Issue: 2
• Publication Date: 2022
• Doi Number: 10.4274/mirt.galenos.2021.43760
• Journal Name: MOLECULAR IMAGING AND RADIONUCLIDE THERAPY
• Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
• Page Numbers: pp.82-88
• Keywords: Solitary pulmonary nodule, PET/CT, radiomic, machine learning, LUNG-CANCER, TOMOGRAPHY
• Open Archive Collection: AVESIS Open Access Collection
• Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

Objectives: 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).