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

• Published in: Scientific reports 2024-04, Vol.14 (1), p.8334-8334, Article 8334
• Main Authors: Nishigaki, Daiki, Suzuki, Yuki, Watabe, Tadashi, Katayama, Daisuke, Kato, Hiroki, Wataya, Tomohiro, Kita, Kosuke, Sato, Junya, Tomiyama, Noriyuki, Kido, Shoji
• Format: Article
• Language: English
• Subjects: 631/1647/245/2092; 631/67/2321; 692/700/1421/1771; Computed tomography; Decision making; Humanities and Social Sciences; Image processing; multidisciplinary; Neural networks; Positron emission tomography; Science; Science (multidisciplinary); Tomography
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-58220-6

Fluorine-18-fluorodeoxyglucose ( 18 F-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 18 F-FDG PET/CT slices as benign or malignant. This retrospective study included 18 F-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