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

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

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.

Methods

We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2009–2019. The study population was then divided into two groups based upon EGFR mutational status. We further employed a DL technique to classify the two groups according to their preoperative magnetic resonance imaging features. Augmentation techniques, transfer learning approach, and post-processing of the predicted results were applied to overcome the relatively small cohort. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC.

Results

Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve value of 0.91 across the 5 validation datasets.

Conclusion

DL-based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.

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Data availability

All the data in this study is available in Sagol Brain Institute, Tel Aviv Medical Center, Tel-Aviv, Israel.

Code availability

Not applicable.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Tel Aviv Medical Center, Affiliated to the Sackler Faculty of Medicine, Tel-Aviv University, 6 Weizman Street, 6423906, Tel-Aviv, Israel

    Oz Haim, Shani Abramov, Ben Shofty, Zvi Ram & Rachel Grossman

  2. Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy

    Claudia Fanizzi & Francesco DiMeco

  3. School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel

    Netanell Avisdris

  4. Sagol Brain Institute, Tel Aviv Medical Center, and the Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv, Israel

    Netanell Avisdris & Moran Artzi

  5. Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA

    Ben Shofty

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  1. Oz Haim
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  9. Rachel Grossman
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Contributions

OH, BS, ZR, MA and RG contributed to the study conception and design. Material preparation and data collection were performed by SA, OH and CF, analysis was performed by MA and NA. The first draft of the manuscript was written by OH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rachel Grossman.

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Conflict of interest

The authors declare no competing interests.

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Tel-Aviv Medical Center, and Fondazio ne IRCCS Istituto Neurologico C. Besta, (IRB approval numbers 0200-10, and 81/2021, respectively).

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Haim, O., Abramov, S., Shofty, B. et al. Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases. J Neurooncol 157, 63–69 (2022). https://doi.org/10.1007/s11060-022-03946-4

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  • DOI: https://doi.org/10.1007/s11060-022-03946-4

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