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April 5, 2026Cancer Research

Abstract 1457: Prediction of gene expression and molecular pathway activity from H&E whole slide images in non-small cell lung cancer.

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Authors

MKMina KhoshdeliMSMuhammad SohaibMQMohammed Qutaish

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Overview

Demonstrates strong predictive models for gene expression in non-small cell lung cancer, indicating new opportunities for diagnosis.

Key Points

  • The research aims to predict gene expression and molecular pathway activity from H&E whole slide images in non-small cell lung cancer.
  • Developed a two-stage framework for feature extraction using Gigapath.
  • Divided H&E images into 256×256 pixel patches and aggregated features using an attention mechanism.
  • Evaluated regression models to predict gene expression and pathway activity.
  • Used a dataset of 67 NSCLC samples with matched RNA-seq for model evaluation.
  • Gigapath-Random Forest regressor showed strong performance with Spearman correlations up to 0.70.
  • Identified distinct pathways critical to NSCLC biology with identifiable histological signatures.
  • Predicted expression levels of 2,223 genes with a Spearman correlation greater than 0.4.

Cite This Study

Khoshdeli et al. (2026) studied this question.

synapsesocial.com/papers/69d1fcfda79560c99a0a2ccehttps://doi.org/10.1158/1538-7445.am2026-1457
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