Litcius/Paper detail

Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications

Carmelo Caldarella, Marina De Risi, Mariangela Massaccesi, Francesco Miccichè, Francesco Bussu, Jacopo Galli, Vittoria Rufini, Lucia Leccisotti

2024Cancers26 citationsDOIOpen Access PDF

Abstract

This article provides an overview of the use of 18F-FDG PET/CT in various clinical scenarios of head–neck squamous cell carcinoma, ranging from initial staging to treatment-response assessment, and post-therapy follow-up, with a focus on the current evidence, debated issues, and innovative applications. Methodological aspects and the most frequent pitfalls in head–neck imaging interpretation are described. In the initial work-up, 18F-FDG PET/CT is recommended in patients with metastatic cervical lymphadenectomy and occult primary tumor; moreover, it is a well-established imaging tool for detecting cervical nodal involvement, distant metastases, and synchronous primary tumors. Various 18F-FDG pre-treatment parameters show prognostic value in terms of disease progression and overall survival. In this scenario, an emerging role is played by radiomics and machine learning. For radiation-treatment planning, 18F-FDG PET/CT provides an accurate delineation of target volumes and treatment adaptation. Due to its high negative predictive value, 18F-FDG PET/CT, performed at least 12 weeks after the completion of chemoradiotherapy, can prevent unnecessary neck dissections. In addition to radiomics and machine learning, emerging applications include PET/MRI, which combines the high soft-tissue contrast of MRI with the metabolic information of PET, and the use of PET radiopharmaceuticals other than 18F-FDG, which can answer specific clinical needs.

Topics & Concepts

MedicineRadiologyPositron emission tomographyHead and neck squamous-cell carcinomaPET-CTRadiation therapyOccultChemoradiotherapyHead and neck cancerRadiomicsNuclear medicinePathologyAlternative medicineHead and Neck Cancer StudiesRadiomics and Machine Learning in Medical ImagingCancer Diagnosis and Treatment