Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer

• Published in: The Journal of nuclear medicine (1978) Vol. 65; no. 4; p. 623
• Main Authors: Kovacs, David G, Ladefoged, Claes N, Andersen, Kim F, Brittain, Jane M, Christensen, Charlotte B, Dejanovic, Danijela, Hansen, Naja L, Loft, Annika, Petersen, Jørgen H, Reichkendler, Michala, Andersen, Flemming L, Fischer, Barbara M
• Format: Journal Article
• Language: English
• Published: New York Society of Nuclear Medicine 01.04.2024
• Subjects: Artificial intelligence; Automation; Biomarkers; Cancer; Computed tomography; Deep learning; Delineation; Fluorine isotopes; Head & neck cancer; Machine learning; Positron emission; Positron emission tomography; Radiation therapy; State-of-the-art reviews; Teaching methods; Tumors; Variability; Variance analysis

Artificial intelligence (AI) may decrease 18F-FDG PET/CT–based gross tumor volume (GTV) delineation variability and automate tumor-volume–derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET G...