AIMC Topic: Stomach Neoplasms

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Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning.

Scientific reports
Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenviro...

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machi...

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.

International journal of nanomedicine
BACKGROUND: Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outco...

An Integrated Radiopathomics Machine Learning Model to Predict Pathological Response to Preoperative Chemotherapy in Gastric Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of pa...

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

Medical physics
BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable f...

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer.

Computers in biology and medicine
BACKGROUND: Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored.

DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.

IEEE journal of biomedical and health informatics
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed di...

Linked Color Imaging with Artificial Intelligence Improves the Detection of Early Gastric Cancer.

Digestive diseases (Basel, Switzerland)
INTRODUCTION: Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detection (CADe) system to detect GC with white light imaging (WLI) and linked color imaging (LCI) modes a...

Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning.

Surgical endoscopy
BACKGROUND: Postoperative pancreatic fistula (POPF) is a critical complication of laparoscopic gastrectomy (LG). However, there are no widely recognized anatomical landmarks to prevent POPF during LG. This study aimed to identify anatomical landmarks...