PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：食道，胃食管,胃恶性肿瘤通常在局部晚期诊断，建议采用多模式治疗以增加生存机会。然而,考虑到治疗反应的显著差异，明确必须完善患者分层.这篇叙述性综述的目的是探索现有证据和影像组学在改善胃胃癌分期和预测治疗反应方面的潜力。
方法：本文的参考文献是通过MEDLINE（PubMed）和Scopus搜索确定的，其术语为“radiomics”，\"纹理分析\",“食道癌”，“胃食管结合部癌”，“食管胃结合部癌”，“胃癌”，“胃癌”，\"暂存\",和“治疗反应”，直至2024年5月。
结果：在所有成像方式下，Radiomics被证明可有效改善食管癌和胃癌的疾病分期和治疗反应预测（TC，MRI,和18F-FDGPET/CT）。关于影像组学应用于胃食管交界处癌的文献资料非常匮乏。与单一放射学方法相比，当整合不同的成像模式时，以及与仅使用放射组学签名相比，将临床与放射组学特征相结合时，放射组学模型表现更好。
结论：影像组学在局部晚期胃腺癌患者的非侵入性分期和预测术前治疗反应方面具有潜力。作为未来的视角,将分子亚组分析纳入临床和影像学特征甚至可能提高这些预测和预后模型的有效性.
方法：本文的参考文献是通过MEDLINE（PubMed）和Scopus搜索确定的，其术语为“radiomics”，\"纹理分析\",“食道癌”，“胃食管结合部癌”，“食管胃结合部癌”，“胃癌”，“胃癌”，\"暂存\",和“治疗反应”，直至2024年5月。
结果：在所有成像方式下，Radiomics被证明可有效改善食管癌和胃癌的疾病分期和治疗反应预测（TC，MRI,和18F-FDGPET/CT）。关于影像组学应用于胃食管交界处癌的文献资料非常匮乏。与单一放射学方法相比，当整合不同的成像模式时，以及与仅使用放射组学签名相比，将临床与放射组学特征相结合时，放射组学模型表现更好。
结论：影像组学在局部晚期胃腺癌患者的非侵入性分期和预测术前治疗反应方面具有潜力。作为未来的视角,将分子亚组分析纳入临床和影像学特征甚至可能提高这些预测和预后模型的有效性.
