Artificial intelligence to identify genetic alterations in conventional histopathology
Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
[HTML][HTML] Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review
Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important
genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with …
genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with …
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …
Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre …
Background Response to immunotherapy in gastric cancer is associated with microsatellite
instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We …
instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We …
Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images
Breast cancer tumor grade is strongly associated with patient survival. In current clinical
practice, pathologists assign tumor grade after visual analysis of tissue specimens …
practice, pathologists assign tumor grade after visual analysis of tissue specimens …
[HTML][HTML] Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application
Background Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key
genetic feature which should be tested in every patient with colorectal cancer (CRC) …
genetic feature which should be tested in every patient with colorectal cancer (CRC) …
Prediction of BRCA gene mutation in breast cancer based on deep learning and histopathology images
X Wang, C Zou, Y Zhang, X Li, C Wang, F Ke… - Frontiers in …, 2021 - frontiersin.org
Background Breast cancer is one of the most common cancers and the leading cause of
death from cancer among women worldwide. The genetic predisposition to breast cancer …
death from cancer among women worldwide. The genetic predisposition to breast cancer …
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient
care in colorectal cancers (CRC), and is associated with response to immunotherapy in all …
care in colorectal cancers (CRC), and is associated with response to immunotherapy in all …
Deep learning-based prediction of molecular tumor biomarkers from H&E: a practical review
HD Couture - Journal of Personalized Medicine, 2022 - mdpi.com
Molecular and genomic properties are critical in selecting cancer treatments to target
individual tumors, particularly for immunotherapy. However, the methods to assess such …
individual tumors, particularly for immunotherapy. However, the methods to assess such …
All you need is color: image based spatial gene expression prediction using neural stain learning
M Dawood, K Branson, NM Rajpoot… - … European Conference on …, 2021 - Springer
Abstract “Is it possible to predict expression levels of different genes at a given spatial
location in the routine histology image of a tumor section by modeling its stain absorption …
location in the routine histology image of a tumor section by modeling its stain absorption …