Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma

Background: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene exp...

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Bibliographic Details
Main Authors: Pierre Murchan, Anne-Marie Baird, Pilib Ó Broin, Orla Sheils, Stephen P. Finn
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Diagnostics
Subjects:
deep learning
computational pathology
biomarkers
survival
lung adenocarcinoma
Online Access:https://www.mdpi.com/2075-4418/14/5/462

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https://www.mdpi.com/2075-4418/14/5/462

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