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

SALİHOĞLU, YAVUZ; Erdemir, Rabiye; Puren, Busra; ÖZDEMİR, SEMRA; Uyulan, Caglar; ERGÜZEL, TÜRKER; Tekin, Huseyin

doi:10.4274/mirt.galenos.2021.43760

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

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

MOLECULAR IMAGING AND RADIONUCLIDE THERAPY, cilt.31, sa.2, ss.82-88, 2022 (ESCI, Scopus, TRDizin)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 31 Sayı: 2
Basım Tarihi: 2022
Doi Numarası: 10.4274/mirt.galenos.2021.43760
Dergi Adı: MOLECULAR IMAGING AND RADIONUCLIDE THERAPY
Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
Sayfa Sayıları: ss.82-88
Anahtar Kelimeler: Solitary pulmonary nodule, PET/CT, radiomic, machine learning, LUNG-CANCER, TOMOGRAPHY
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

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).