Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

Journal: Journal of neuro-oncology
Published Date:

Abstract

PURPOSE: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM.

Authors

  • Oz Haim
    Division of Neurosurgery, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel.
  • Shani Abramov
    Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Ben Shofty
    Division of Neurosurgery, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel.
  • Claudia Fanizzi
    Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy.
  • Francesco DiMeco
    Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, via Celoria 11, 20133, Milan, Italy.
  • Netanell Avisdris
    School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Zvi Ram
    Division of Neurosurgery, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel.
  • Moran Artzi
    Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Rachel Grossman
    Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.