Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.

Journal: Translational lung cancer research
Published Date: May 30, 2025

Abstract

BACKGROUND: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) accounts for 85% of cases, with oncogenic alterations like , and guiding targeted therapies. Their prevalence varies by ethnicity, smoking status, and gender. Advances in artificial intelligence (AI) enable molecular biomarker prediction from hematoxylin and eosin-stained whole-slide images (H&E WSIs), offering a non-invasive approach to precision oncology. This review assesses deep learning (DL) models predicting oncogenic drivers in NSCLC from H&E WSIs and their diagnostic accuracy.

Authors

  • Rafael Parra-Medina
    Research Institute, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Gabriela Guerron-Gomez
    Research Institute, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Daniel Mendivelso-González
    Department of Pathology, Instituto Nacional de Cancerología, Bogotá, Colombia.
  • Javier Hernan Gil-Gómez
    Research Institute, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Juan Pablo Alzate
    Department of Epidemiology, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Marcela Gomez-Suarez
    Research Institute, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Jose Fernando Polo
    Department of Pathology, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • John Jaime Sprockel
    Research Institute, Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia.
  • Andrés Mosquera-Zamudio
    Pathology Department Hospital Clínico Universitario de Valencia, Universidad de Valencia, Valencia, Spain. amosquera@incliva.es.

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