Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography

Abstract

Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (¹⁸F)-fluorodeoxyglucose positron emission tomography/computed tomography ([¹⁸F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (¹⁸F)-FDG PET/CT in the management of cancer treated by radiation therapy.

Keywords: Apprentissage automatique; Apprentissage profond; Artificial intelligence; Deep learning; Intelligence artificielle; Machine learning; Radiation therapy; Radiomics; Radiomique; Radiothérapie.