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

Content Provider	Europe PMC
Author	Salihoğlu, Yavuz Sami Uslu Erdemir, Rabiye Aydur Püren, Büşra Özdemir, Semra Uyulan, Çağlar Ergüzel, Türker Tekin Tekin, Hüseyin Ozan
Copyright Year	2022
Abstract	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 on 18F-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.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9246312&blobtype=pdf
ISSN	21461414
Journal	Molecular Imaging and Radionuclide Therapy [Mol Imaging Radionucl Ther]
Volume Number	31
DOI	10.4274/mirt.galenos.2021.43760
PubMed Central reference number	PMC9246312
Issue Number	2
PubMed reference number	35770958
e-ISSN	21471959
Language	English
Publisher	Galenos Publishing
Publisher Date	2022-06-01
Access Restriction	Open
Rights License	This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ©Copyright 2022 by Turkish Society of Nuclear Medicine | Molecular Imaging and Radionuclide Therapy published by Galenos Yayınevi.
Subject Keyword	Solitary pulmonary nodule PET/CT radiomic machine learning
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging