Back to search

Article

Computational biomarker predicts lung ICI response via deep learning-driven hierarchical spatial modelling from H&E

2022-01-21

Abstract excerpt

<title>Abstract</title> <p>Determining which lung cancer patients are likely to respond to immune checkpoint inhibitors (ICI) remains a crucial challenge. Existing FDA-approved biomarkers lack sensitivity and specificity for identifying treatment candidates. To overcome this problem, we present a computational biomarker for predicting ICI response directly from routine H&E stained whole slide images of the initia...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
001a26db-03ef-5ff3-bf5f-b61208b62ddf
DOI
10.21203/rs.3.rs-1251762/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Computational biomarker predicts lung ICI response via deep learning-driven hierarchical spatial modelling from H&amp;EDOI 10.21203/rs.3.rs-1251762/v1
Development and validation of an interpretable prediction model using spatial patterns of tumor-infiltrating lymphocytes in H&E-stained whole-slide images for immune subtyping of lung adenocarcinoma.Semantic proximity
Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry ImagesSemantic proximity
Spatial biomarker-driven deep learning model via digital pathology predicts response to PI3K inhibitor buparlisib in head and neck squamous cell carcinomaSemantic proximity
Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2Semantic proximity
Interpretable Deep Learning Reveals Biologically Relevant Spatial Gene Expression Patterns in Lung Tumors and their MicroenvironmentSemantic proximity
DeepMPS: Development and validation of a deep learning model for whole slide image base prognostic prediction of low grade Lung adenocarcinoma patientsSemantic proximity
Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancerSemantic proximity
Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancerSemantic proximity
MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide imagesSemantic proximity
Predicting tumour mutational burden from histopathological images using multiscale deep learningSemantic proximity
Prediction of cancer treatment response from histopathology images through imputed transcriptomicsSemantic proximity
Digital Pathology Predicts Immunotherapy Response and Maps Spatial Biomarkers in Gastric CancerSemantic proximity
Select a neighboring publication to make it the new centre.