Article

Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

2024-06-13

Abstract excerpt

Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a non-destructive manner. In this study, we demonstrate the feasibility of generating virtual stains for hematoxylin and eosin (H&E) and a multiplex immunofluorescence (mIF) immuno-oncology panel (DAPI, PanCK, PD-L1, CD3, C...

Topics

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

Identifiers and source

Literature Corpus work: 17694bc8-4d74-53e0-b7b6-2e60f636c75f
DOI: 10.1101/2024.06.12.24308841

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung CancerDOI 10.1101/2024.06.12.24308841Read

<i>ROSIE</i> : AI generation of multiplex immunofluorescence staining from histopathology imagesSemantic proximity Read

An end-to-end platform for digital pathology using hyperspectral autofluorescence microscopy and deep learning based virtual histologySemantic proximity Read

Assessment of an AI virtual staining model performance across same and serial tissue sections using CD3 <sup>+</sup> T cell ground truthSemantic proximity Read

Distinct tumor-immune ecologies in NSCLC patients predict progression and define a clinical biomarker of therapy responseSemantic proximity Read

Spatial Clustering Analysis with Spectral Imaging-based Single-Step Multiplex Immunofluorescence (SISS-mIF) for Assisting Histological DiagnosisSemantic proximity Read

CLEAR-IT: Contrastive Learning to Capture the Immune Composition of Tumor MicroenvironmentsSemantic proximity Read

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.Semantic proximity Read

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancerSemantic proximity Read

ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex ImagingSemantic proximity Read

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.Semantic proximity Read

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysisSemantic proximity Read

Microenvironmental information significantly improves the recognition of cell types in human lung cancer patientsSemantic proximity Read