Vision transformer to differentiate between benign and malignant slices in 18F-FDG PET/CT

Autor: Daiki Nishigaki, Yuki Suzuki, Tadashi Watabe, Daisuke Katayama, Hiroki Kato, Tomohiro Wataya, Kosuke Kita, Junya Sato, Noriyuki Tomiyama, Shoji Kido
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-58220-6
Popis: Abstract Fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) is widely used for the detection, diagnosis, and clinical decision-making in oncological diseases. However, in daily medical practice, it is often difficult to make clinical decisions because of physiological FDG uptake or cancers with poor FDG uptake. False negative clinical diagnoses of malignant lesions are critical issues that require attention. In this study, Vision Transformer (ViT) was used to automatically classify 18F-FDG PET/CT slices as benign or malignant. This retrospective study included 18F-FDG PET/CT data of 207 (143 malignant and 64 benign) patients from a medical institute to train and test our models. The ViT model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 [95% CI 0.89, 0.91], which was superior to the baseline Convolutional Neural Network (CNN) models (EfficientNet, 0.87 [95% CI 0.86, 0.88], P
Databáze: Directory of Open Access Journals
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