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Παρασκευή 25 Αυγούστου 2017

Role of interim 18 F-FDG-PET/CT for the early prediction of clinical outcomes of Non-Small Cell Lung Cancer (NSCLC) during radiotherapy or chemo-radiotherapy. A systematic review

Abstract

Background

Non-Small Cell Lung Cancer (NSCLC) is characterized by aggressiveness and includes the majority of thorax malignancies. The possibility of early stratification of patients as responsive and non-responsive to radiotherapy with a non-invasive method is extremely appealing. The distribution of the Fluorodeoxyglucose (18F–FDG) in tumours, provided by Positron-Emission-Tomography (PET) images, has been proved to be useful to assess the initial staging of the disease, recurrence, and response to chemotherapy and chemo-radiotherapy (CRT).

Objectives

In the last years, particular efforts have been focused on the possibility of using ad interim 18F–FDG PET (FDGint) to evaluate response already in the course of radiotherapy. However, controversial findings have been reported for various malignancies, although several results would support the use of FDGint for individual therapeutic decisions, at least in some pathologies. The objective of the present review is to assemble comprehensively the literature concerning NSCLC, to evaluate where and whether FDGint may offer predictive potential.

Methods

Several searches were completed on Medline and the Embase database, combining different keywords. Original papers published in the English language from 2005 to 2016 with studies involving FDGint in patients affected by NSCLC and treated with radiation therapy or chemo-radiotherapy only were chosen.

Results

Twenty-one studies out of 970 in Pubmed and 1256 in Embase were selected, reporting on 627 patients.

Conclusion

Certainly, the lack of univocal PET parameters was identified as a major drawback, while standardization would be required for best practice. In any case, all these papers denoted FDGint as promising and a challenging examination for early assessment of outcomes during CRT, sustaining its predictivity in lung cancer.



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