Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
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 …
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically
approved applications use this technology. Most approaches, however, predict categorical …
approved applications use this technology. Most approaches, however, predict categorical …
[HTML][HTML] Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal …
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 …
cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a …
Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study
R Yamashita, J Long, T Longacre, L Peng… - The Lancet …, 2021 - thelancet.com
Background Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical
decision making, as it identifies patients with differential treatment response and prognosis …
decision making, as it identifies patients with differential treatment response and prognosis …
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Background For virtually every patient with colorectal cancer (CRC), hematoxylin–eosin
(HE)–stained tissue slides are available. These images contain quantitative information …
(HE)–stained tissue slides are available. These images contain quantitative information …
End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study
X Jiang, M Hoffmeister, H Brenner, HS Muti… - The Lancet Digital …, 2024 - thelancet.com
Background Precise prognosis prediction in patients with colorectal cancer (ie, forecasting
survival) is pivotal for individualised treatment and care. Histopathological tissue slides of …
survival) is pivotal for individualised treatment and care. Histopathological tissue slides of …
[HTML][HTML] Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune
checkpoint blockade (ICB) therapy. However, current MSI identification methods are not …
checkpoint blockade (ICB) therapy. However, current MSI identification methods are not …
Interpretable survival prediction for colorectal cancer using deep learning
Deriving interpretable prognostic features from deep-learning-based prognostic
histopathology models remains a challenge. In this study, we developed a deep learning …
histopathology models remains a challenge. In this study, we developed a deep learning …