AI potential in PET/CT cancer imaging.

Abstract

Positron emission tomography/computed tomography (PET/CT) is a hybrid medical imaging technique that combines PET and CT to provide detailed images of the body's anatomical structures and metabolic activity. It is frequently used for oncology and other medical diagnoses. This overview aims to examine how artificial intelligence (AI) has been used in PET/CT, based on recent state-of-art. There are a number of clinical questions in Nuclear Medicine, and AI could provide answers, having the capability to enhance various aspects of medical imaging. The overview focuses on how machine learning (ML) and deep learning (DL), enhance tumor segmentation, classification, diagnosis, disease-free survival prediction and treatment response prediction in oncology. The analysis showed that the application of AI provides reliable results, especially in the fields of classification and diagnosis. In addition, radiomics is a novel research field enabling quantitative analysis of medical images through feature extraction, utilized for AI model implementation. Despite these advances, addressing issues such as dataset size, standardization, and ethical concerns are essential for broad clinical integration of AI in PET/CT oncology imaging.