AIMC Topic: Esophageal Neoplasms
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Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 24, 2020
Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dyspla...
Technique for Robotic Transhiatal Esophagectomy.
Annals of surgical oncology
Jan 13, 2020
Minimally invasive esophagectomy is increasing performed for cancers of the esophagus and gastroesophageal junction. This video demonstrates the setup and key steps for a robotic transhiatal esophagectomy with a cervical anastomosis.
Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video).
Gastrointestinal endoscopy
Jan 11, 2020
BACKGROUND AND AIMS: The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-light and virtual chromoendoscopy still remains challenging. The aim of this study was to assess wheth...
Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video).
Gastrointestinal endoscopy
Jan 10, 2020
BACKGROUND AND AIMS: We assessed the preliminary diagnostic accuracy of a recently developed computer-aided detection (CAD) system for detection of Barrett's neoplasia during live endoscopic procedures.
Finding Barrett's oesophagus: is there a machine learning approach in our future?
The Lancet. Digital health
Dec 23, 2019
A hybrid automated treatment planning solution for esophageal cancer.
Radiation oncology (London, England)
Dec 19, 2019
OBJECTIVE: This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer.
Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.
Journal of cellular and molecular medicine
Nov 19, 2019
Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer...
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides.
JAMA network open
Nov 1, 2019
IMPORTANCE: Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown pro...
Endoscopic detection and differentiation of esophageal lesions using a deep neural network.
Gastrointestinal endoscopy
Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Optical Imaging
Esophageal Squamous Cell Carcinoma
Esophageal Diseases
Narrow Band Imaging
Sensitivity and Specificity
Esophageal Neoplasms
Aged
Image Processing, Computer-Assisted
Male
Observer Variation
Middle Aged
Aged, 80 and over
Humans
Neural Networks, Computer
Neoplasm Invasiveness
Deep Learning
Adult
Precancerous Conditions
Esophagus
Female
Reproducibility of Results
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