18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis.

Abstract

The best treatment strategy for oesophageal cancer patients achieving a complete clinical response after neoadjuvant chemoradiation is a burning topic. The available diagnostic tools, such as 18F-FDG PET/CT performed routinely, cannot accurately evaluate the presence or absence of the residual tumour. The emerging field of radiomics may encounter the critical challenge of personalised treatment. Radiomics is based on medical image analysis, executed by extracting information from many image features; it has been shown to provide valuable information for predicting treatment responses in oesophageal cancer. This systematic review with a meta-analysis aims to provide current evidence of 18F-FDG PET-based radiomics in predicting response treatments following neoadjuvant chemoradiotherapy in oesophageal cancer. A comprehensive literature review identified 1160 studies, of which five were finally included in the study. Our findings provided that pooled Area Under the Curve (AUC) of the five selected studies was relatively high at 0.821 (95% CI: 0.737-0.904) and not influenced by the sample size of the studies. Radiomics models exhibited a good performance in predicting pathological complete responses (pCRs). This review further strengthens the great potential of 18F-FDG PET-based radiomics to predict pCRs in oesophageal cancer patients who underwent neoadjuvant chemoradiotherapy. Additionally, our review imparts additional support to prospective studies on 18F-FDG PET radiomics for a tailored treatment strategy of oesophageal cancer patients.

Systematic Review Registration:

https://www.crd.york.ac.uk/prospero/, identifier CRD42021274636.

• Authors: Letizia Deantonio, Maria Luisa Garo, Gaetano Paone, Maria Carla Valli, Stefano Cappio, Davide La Regina, Marco Cefali, Maria Celeste Palmarocchi, Alberto Vannelli, Sara De Dosso
• Journal: Frontiers in oncology (Front Oncol) Vol. 12 Pg. 861638 ( 2022) ISSN: 2234-943X [Print] Switzerland
• PMID: 35371989 (Publication Type: Systematic Review)
• Copyright: Copyright © 2022 Deantonio, Garo, Paone, Valli, Cappio, La Regina, Cefali, Palmarocchi, Vannelli and De Dosso.