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Gastrointestinal Neoplasms

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Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks.

Frontiers in immunology
OBJECTIVES: Accurate determination of gastrointestinal tumor malignancy is a crucial focus of clinical research. Constructing coagulation index models using big data is feasible to achieve this goal. This study builds various prediction models throug...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

Cancer letters
GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies have reduced the mortality rates for GI malignancies, underscoring the main role of early detection ...

Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.

Briefings in functional genomics
OBJECTIVE: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications.

Cancer letters
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal t...

A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

The Journal of pathology
Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene seq...

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images.

BMC medical informatics and decision making
Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computat...

RTGN: Robust Traditional Chinese Medicine Graph Networks for Patient Similarity Learning.

IEEE journal of biomedical and health informatics
Traditional Chinese Medicine (TCM) boasts a long history and a unique diagnostic and therapeutic paradigm. Integrating TCM with Western medicine and modern medical devices has yielded numerous successful cases in recent years. TCM treatment has devel...

Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.

BMC gastroenterology
BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This...

Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.

Journal of translational medicine
BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical early symptoms, causing patients to miss optimal treatment opportunities. Traditional endoscopic and ...