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18F-FDG PET/CT in Restaging and Evaluation of Response to Therapy in Lung Cancer: State of the Art

BACKGROUND: Metabolic information provided by 18F-FDG PET/CT are useful for initial staging, therapy planning, response evaluation, and to a lesser extent for the follow-up of non-small cell lung cancer (NSCLC). To date, there are no established clinical guidelines in treatment response and early de...

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Detalles Bibliográficos
Autores principales: Castello, Angelo, Rossi, Sabrina, Lopci, Egesta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493792/
https://www.ncbi.nlm.nih.gov/pubmed/31886757
http://dx.doi.org/10.2174/1874471013666191230144821
Descripción
Sumario:BACKGROUND: Metabolic information provided by 18F-FDG PET/CT are useful for initial staging, therapy planning, response evaluation, and to a lesser extent for the follow-up of non-small cell lung cancer (NSCLC). To date, there are no established clinical guidelines in treatment response and early detection of recurrence. OBJECTIVE: To provide an overview of 18F-FDG PET/CT in NSCLC and in particular, to discuss its utility in treatment response evaluation and restaging of lung cancer. METHODS: A comprehensive search was used based on PubMed results. From all studies published in English those that explored the role of 18F-FDG PET/CT in the treatment response scenario were selected. RESULTS: Several studies have demonstrated that modifications in metabolic activity, expressed by changes in SUV both in the primary tumor as well as in regional lymph nodes, are associated with tumor response and survival. Beside SUV, other metabolic parameters (i.e. MTV, TLG, and percentage changes) are emerging to be helpful for predicting clinical outcomes. CONCLUSION: 18F-FDG parameters appear to be promising factors for evaluating treatment response and for detecting recurrences, although larger prospective trials are needed to confirm these evidences and to determine optimal cut-off values.