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Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience.

作者信息

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

机构信息

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.

出版信息

Cancers (Basel). 2024 Jul 26;16(15):2664. doi: 10.3390/cancers16152664.


DOI:10.3390/cancers16152664
PMID:39123392
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11311587/
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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94aa/11311587/429ac8116e20/cancers-16-02664-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94aa/11311587/ebd401807629/cancers-16-02664-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94aa/11311587/429ac8116e20/cancers-16-02664-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94aa/11311587/ebd401807629/cancers-16-02664-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94aa/11311587/429ac8116e20/cancers-16-02664-g002.jpg

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Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience.

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引用本文的文献

[1]
Current Role of Artificial Intelligence in the Management of Esophageal Cancer.

J Clin Med. 2025-3-9

本文引用的文献

[1]
Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer.

World J Clin Oncol. 2024-3-24

[2]
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

CA Cancer J Clin. 2024

[3]
Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3, CD4, and CD8 tumor-infiltrating lymphocytes in advanced gastric carcinoma.

Front Oncol. 2024-3-14

[4]
Prognostic implications of tumor-infiltrating lymphocytes within the tumor microenvironment in gastric cancer.

J Gastrointest Surg. 2024-2

[5]
The role of baseline 2-[ F]-FDG-PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis.

Hematol Oncol. 2024-3

[6]
Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis.

Eur J Radiol. 2024-4

[7]
Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics-Clinicopathological Nomogram.

Cancers (Basel). 2024-1-31

[8]
Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer.

Abdom Radiol (NY). 2024-5

[9]
Prediction of HER2 Expression in Gastric Adenocarcinoma Based On Preoperative Noninvasive Multimodal F-FDG PET/CT Imaging.

Acad Radiol. 2024-8

[10]
MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study.

Cancer Imaging. 2024-1-23

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