AIMC Topic: Stomach Neoplasms
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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
Aug 12, 2024
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
Aug 6, 2024
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)
Aug 5, 2024
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
Aug 2, 2024
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...
Deep learning is necessary for safety regulation in predicting malnutrition in gastric cancer patients.
Clinical nutrition (Edinburgh, Scotland)
Aug 2, 2024
Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.
Medical molecular morphology
Aug 1, 2024
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients...
Development and validation of a machine learning-based F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jul 30, 2024
BACKGROUND: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-...
Explainable machine learning models for early gastric cancer diagnosis.
Scientific reports
Jul 29, 2024
Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive...
Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.
Digestion
Jul 26, 2024
BACKGROUND: Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestina...
Colorectal Neoplasms
Diagnosis, Computer-Assisted
Deep Learning
Image Processing, Computer-Assisted
Artificial Intelligence
Stomach Neoplasms
Sensitivity and Specificity
Esophageal Neoplasms
Esophageal Squamous Cell Carcinoma
Humans
Adenocarcinoma
Endoscopy, Digestive System
Endoscopy, Gastrointestinal
Gastrointestinal Neoplasms
Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer.
Scientific reports
Jul 13, 2024
The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospect...
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