AIMC Topic: Stomach Neoplasms
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A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Dec 15, 2022
BACKGROUND: White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasm...
Simple add-on robotic traction device to the conventional endoscope: Will it be an efficient and safe tool for novice training on endoscopic submucosal dissection?
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Nov 28, 2022
Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.
Cancer science
Nov 22, 2022
Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based mo...
Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Nov 17, 2022
OBJECTIVES: Endoscopists' abilities to diagnose early gastric cancers (EGCs) vary, especially between specialists and nonspecialists. We developed an artificial intelligence (AI)-based diagnostic support tool "Tango" to differentiate EGCs and compare...
Deep learning-based diagnostic model for predicting complications after gastrectomy.
Asian journal of endoscopic surgery
Nov 9, 2022
BACKGROUND: Gastric cancer is one of the leading causes of cancer deaths, and gastrectomy with lymph node dissection is the mainstay of treatment. Despite clinician efforts and advances in surgical methods, the incidence of complications after gastre...
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
Nov 8, 2022
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
Nov 2, 2022
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
Oct 27, 2022
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
Oct 21, 2022
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
Oct 19, 2022
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...
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