AIMC Topic: Stomach Neoplasms

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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...

Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images.

Scandinavian journal of gastroenterology
OBJECTIVES: Precise delineation of early gastric cancer (EGC) margins is essential for complete resection during endoscopic submucosal dissection. This study aimed to develop deep learning-based models for EGC boundary detection in narrow-band imagin...

Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.

Frontiers in immunology
INTRODUCTION: The rise in cases of Gastric Cancer has increased in recent times and demands accurate and timely detection to improve patients' well-being. The traditional cancer detection techniques face issues of explainability and precision posing ...

Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.

Nature communications
In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently and without damaging the tissues during serial sectioning and staining remains a formidable challenge. We address this by integrating holotomography w...

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles inĀ gastric cancer, colorectal cancer, and inflammatory bowel disease.

Journal of translational medicine
INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This...

Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study.

Gut
BACKGROUND AND OBJECTIVE: Gastric cancer (GC) remains a prevalent and preventable disease, yet accurate early diagnostic methods are lacking. Exosome non-coding RNAs (ncRNAs), a type of liquid biopsy, have emerged as promising diagnostic biomarkers f...

Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.

BMC cancer
BACKGROUND: Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most impo...

The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.

Abdominal radiology (New York)
PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

ChatGPT-4o outperforms gemini advanced in assisting multidisciplinary decision-making for advanced gastric cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND & AIMS: The treatment of advanced gastric cancer (GC) requires precise and comprehensive clinical decision-making. Artificial intelligence (AI) chatbots offer potential tools to enhance multidisciplinary team (MDT) discussions. This study ...