AIMC Topic: Microsatellite Instability
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Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.
International journal of cancer
Aug 27, 2022
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...
Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction.
Scientific reports
Jul 18, 2022
Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low-level approach using feature extractors. Although the advantages and disadvantages of both approach...
Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.
Laboratory investigation; a journal of technical methods and pathology
Feb 17, 2022
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-cons...
Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning.
Scientific reports
Nov 22, 2021
Immune checkpoint inhibitor (ICI) therapy is widely used but effective only in a subset of gastric cancers. Epstein-Barr virus (EBV)-positive and microsatellite instability (MSI) / mismatch repair deficient (dMMR) tumors have been reported to be high...
Machine Learning
Software
Algorithms
Databases, Factual
Humans
Neural Networks, Computer
Microsatellite Instability
DNA Mismatch Repair
Immunohistochemistry
Genome, Human
Lymphocytosis
Immunotherapy
Treatment Outcome
Herpesvirus 4, Human
Area Under Curve
Reproducibility of Results
Stomach Neoplasms
Deep Learning
Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.
The Lancet. Digital health
Oct 19, 2021
BACKGROUND: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pa...
Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.
The Lancet. Digital health
Aug 17, 2021
BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers ...
Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.
Cancer medicine
May 8, 2021
BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning m...
Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.
International journal of cancer
Apr 27, 2021
High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classi...
Deep learning detects genetic alterations in cancer histology generated by adversarial networks.
The Journal of pathology
Mar 16, 2021
Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection o...
Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers.
Gut
Nov 19, 2020
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complemen...
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