Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
Keywords: Artificial intelligence; FDG PET/CT; Lung cancer; Molecular imaging; Radiomics.
Copyright © 2020 Elsevier Inc. All rights reserved.