AIMC Topic: Stomach Neoplasms

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Differentiating Gastric Cancers from Acid Peptic Diseases through Integrative Targeted Proteomics and Machine Learning Approaches.

Journal of proteome research
Gastric cancers (GCs) are often diagnosed in advanced stages owing to nonspecific early symptoms resembling Acid Peptic Diseases (APDs). Despite recent efforts, a simple, liquid biopsy-based multiprotein panel prediagnostic assay capable of different...

A lightweight YOLOv8-based model for gastric cancer detection.

Computers in biology and medicine
Recent research on deep learning-based gastric cancer detection has demonstrated high performance, with capabilities comparable to or exceeding those of medical professionals. However, the performance of deep learning models depends on the performanc...

A fully annotated pathology slide dataset for early gastric cancer and precancerous lesions.

Scientific data
Gastric cancer, a significant global health concern, exhibits high morbidity and mortality, especially in advanced stages. Timely diagnosis and intervention are crucial for improving patient outcomes, with Endoscopic Submucosal Dissection (ESD) playi...

Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer.

BMC cancer
BACKGROUND: Static carcinoembryonic antigen (CEA) levels are well‑established prognostic markers in patients with gastric cancer, but the significance of their dynamic trajectories over time has rarely been reported.

Lectin-affinity glycosylation pattern analysis of plasma extracellular vesicles: An all-in-one clinical assessment for gastric cancer diagnosis and treatment.

Cancer letters
Extracellular vesicles (EVs) exhibit extensive glycosylation modifications, which are promising biomarkers for gastric cancer (GC). However, EV glycomics and the potential application of EV glycosylation patterns in liquid biopsy remain largely unexp...

Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning.

Cancer letters
Immunotherapy has become a cornerstone in the treatment of advanced gastric cancer (GC). However, identifying reliable predictive biomarkers remains a considerable challenge. This study demonstrates the potential of integrating multimodal baseline da...

Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer.

Scientific reports
To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectiv...

Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification.

Nature communications
Glycosylation changes are closely related to various diseases, including cancer. The quantitative analysis of site-specific glycans at proteomics scale remains challenging due to low glycopeptide spectra interpretation. Here, we present GlyPep-Quant,...

The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations.

Nature communications
Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability...