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Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma

  • RESEARCH ARTICLE
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Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Background

Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, we proposed a deep learning model using non-invasive CT images to predict EGFR mutation status with robustness and generalizability.

Methods

A total of 525 patients were enrolled at the local hospital to serve as the internal data set for model training and validation. In addition, a cohort of 30 patients from the publicly available Cancer Imaging Archive Data Set was selected for external testing. All patients underwent plain chest CT, and their EGFR mutation status labels were categorized as either mutant or wild type. The CT images were analyzed using a self-attention-based ViT-B/16 model to predict the EGFR mutation status, and the model’s performance was evaluated. To produce an attention map indicating the suspicious locations of EGFR mutations, Grad-CAM was utilized.

Results

The ViT deep learning model achieved impressive results, with an accuracy of 0.848, an AUC of 0.868, a sensitivity of 0.924, and a specificity of 0.718 on the validation cohort. Furthermore, in the external test cohort, the model achieved comparable performances, with an accuracy of 0.833, an AUC of 0.885, a sensitivity of 0.900, and a specificity of 0.800.

Conclusions

The ViT model demonstrates a high level of accuracy in predicting the EGFR mutation status of lung adenocarcinoma patients. Moreover, with the aid of attention maps, the model can assist clinicians in making informed clinical decisions.

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Data availability

The data from The First Affiliated Hospital of Wenzhou Medical University that support the findings of this study are not publicly available due to intellectual property considerations. However, the data used for external validation in this study are available in The Cancer Imaging Archive (TCIA), as detailed in references [14, 15].

Abbreviations

EGFR:

Epidermal growth factor receptor

CT:

Computed tomography

ViT:

Vision transformer

AUC:

Area-under-the-ROC-curve analysis

Grad-CAM:

Gradient-weighted class activation mapping

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Acknowledgements

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

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Author notes

  1. Luoqi Weng and Yilun Xu should be regard as co-first authors.

Authors and Affiliations

  1. Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China

    Luoqi Weng, Yuhan Chen & Chengshui Chen

  2. Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China

    Yilun Xu

  3. Department of Respiratory Medicine, Shaoxing People’s Hospital, Shaoxing, 312000, Zhejiang, China

    Qinqing Qian

  4. Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China

    Jie Pan & Huang Su

  5. Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China

    Jie Pan & Huang Su

  6. Department of Gastroenterology, The Second Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China

    Jie Pan & Huang Su

Authors
  1. Luoqi Weng
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LW, YX and HS. The first draft of the manuscript was written by LW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Huang Su.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This retrospective study had received approval from the ethics committee of our institution (Approval No. KY2022-R174).

Informed consent

As all data used in the study had been anonymized, we were not required to obtain informed consent from the patients.

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Weng, L., Xu, Y., Chen, Y. et al. Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma. Clin Transl Oncol (2024). https://doi.org/10.1007/s12094-023-03366-4

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