Garbarino Giovanni Maria, Polici Michela, Caruso Damiano, Laghi Andrea, Mercantini Paolo, Pilozzi Emanuela, van Berge Henegouwen Mark I, Gisbertz Suzanne S, van Grieken Nicole C T, Berardi Eva, Costa Gianluca
Department of General Surgery, Sant' Eugenio Hospital, ASL RM 2, 00144 Rome, Italy.
Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea Hospital, 00189 Rome, Italy.
Cancers (Basel). 2024 Jul 26;16(15):2664. doi: 10.3390/cancers16152664.
BACKGROUND: Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. METHODS: The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. RESULTS: Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. CONCLUSIONS: Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.
Zhonghua Wei Chang Wai Ke Za Zhi. 2018-10-25
J Clin Med. 2025-3-9