TY  - JOUR
TI  - Diagnostic Performance of Machine Learning Models Based on
18
F-FDG PET/CT Radiomic Features in the Classification of Solitary
Pulmonary Nodules
AB  - Objectives: This study aimed to evaluate the ability of 18fluorine-fluorodeoxyglucose (18F-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 on18F-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 ~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 18F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between
benign and malignant lung nodules
AU  - OZDEMİR, Semra
AU  - ERGÜZEL, Türker Tekin
AU  - USLU ERDEMİR, Rabiye
AU  - Salihoğlu, Yavuz Sami
AU  - PÜREN, Büşra Aydur
AU  - UYULAN, Çağlar
AU  - TEKİN, Hüseyin Ozan
DO  - 10.4274/mirt.galenos.2021.43760
PY  - 2022
JO  - Molecular Imaging and Radionuclide Therapy
VL  - 31
IS  - 2
SN  - 2146-1414
SP  - 82
EP  - 88
DB  - TRDizin
UR  - http://search/yayin/detay/535014
ER  -