AIMC Topic: Esophageal Neoplasms

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Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.

BMC medical imaging
PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph nod...

Biomarker risk stratification with capsule sponge in the surveillance of Barrett's oesophagus: prospective evaluation of UK real-world implementation.

Lancet (London, England)
BACKGROUND: Endoscopic surveillance is the clinical standard for Barrett's oesophagus, but its effectiveness is inconsistent. We have developed a test comprising a pan-oesophageal cell collection device coupled with biomarkers to stratify patients in...

Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To investigate the feasibility and accuracy of using deep learning and dosiomics features, as well as their combination with dose-volume histogram (DVH) parameters and clinical factors to predict grade 4 radiation-induced lymphopenia (G4RIL)...

Cone-beam computed tomography-based online adaptive radiotherapy of esophageal cancer in the neoadjuvant setting: Dosimetric analysis, toxicity and treatment response.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Chemoradiotherapy (CRT) followed by surgery is a treatment option for esophageal cancer (EC). However, concerns persist regarding cardiopulmonary toxicity and inconsistent daily target coverage due to anatomical changes. To ad...

Predicting clinical outcomes using 18F-FDG PET/CT-based radiomic features and machine learning algorithms in patients with esophageal cancer.

Nuclear medicine communications
OBJECTIVE: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortal...

Immunohistochemical biomarker scoring in gastroesophageal cancers: Can computers help us?

Pathology, research and practice
The increasing complexity of cancer diagnostics and treatment selection has placed a growing burden on pathologists, particularly in the evaluation of immunohistochemical (IHC) biomarkers. In gastroesophageal cancers (GEC), both adenocarcinoma and sq...

An integrated analytical approach for biomarker discovery in esophageal cancer: Combining trace element and oxidative stress profiling with machine learning.

Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS)
BACKGROUND: Early detection of esophageal squamous cell carcinoma (ESCC) significantly improves survival rates, yet reliable biochemical biomarkers for early diagnosis remain limited. The aim of this study is to identify potential early diagnostic bi...

Radiomics applications in the modern management of esophageal squamous cell carcinoma.

Medical oncology (Northwood, London, England)
Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing ...

Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.

BMC gastroenterology
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...