AIMC Topic: Stomach Neoplasms

Clear Filters Showing 211 to 220 of 482 articles

Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.

Academic radiology
RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological...

Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.

Scientific reports
The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal E...

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

Computational and mathematical methods in medicine
METHODS: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottlen...

Intraoperative near-infrared lymphography with indocyanine green may aid lymph node dissection during robot-assisted resection of gastroesophageal junction cancer.

Surgical endoscopy
BACKGROUND: Adequate lymphadenectomy during gastroesophageal junction (GEJ) cancer resection is essential, because lymph node (LN) metastasis correlates with increased recurrence risk. Fluorescence lymphography with indocyanine green (ICG) has been u...

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.

Artificial intelligence in medicine
Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a de...

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.

JAMA network open
IMPORTANCE: Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid...

Robotic radical distal gastrectomy for gastric cancer using the soft coagulation scissors technique.

Journal of robotic surgery
We have developed a novel technique for safe and precise lymph-node dissection during robotic gastrectomy for gastric cancer using monopolar curved scissors with soft coagulation. This technique is called the soft coagulation scissors technique. The ...

Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND AND STUDY AIMS: The diagnostic ability of endoscopists to determine invasion depth of early gastric cancer is not favorable. We designed an artificial intelligence (AI) classifier for differentiating intramucosal and submucosal gastric can...

Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.

International journal of cancer
Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furthermore, because immune checkpoint inhibitors are much more effective in tumors with high level of MSI (MSI-H), MSI status is routinely tested in multi...

Impact of Hospital Volume on Utilization of Minimally Invasive Surgery for Gastric Cancer.

The American surgeon
BACKGROUND: Minimally invasive surgery (MIS) for gastric cancer is increasingly performed. The purpose of this study is to evaluate trends in utilization of laparoscopic and robotic techniques compared to open surgery as well as utilization based on ...