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Updated: Apr 19, 2026

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Predicting primary resistance to third-generation EGFR-TKIs in lung adenocarcinoma using a multisource cross-modal

Yunfan Wang1, Ke Min1, Li Tao2

  • 1Department of Oncology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, China.

NPJ Precision Oncology
|April 17, 2026
PubMed

Insights

A new AI model, MC-Trans, accurately predicts primary resistance to third-generation EGFR-TKIs in lung cancer patients. This artificial intelligence tool shows promise in guiding treatment decisions for lung adenocarcinoma.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Lung adenocarcinoma patients often develop primary resistance to third-generation EGFR-TKIs.
  • Predicting this resistance is crucial for effective treatment selection.
  • Current prediction methods have limitations.

Purpose of the Study:

  • To evaluate a multisource cross-modal Transformer (MC-Trans) model for predicting primary resistance to third-generation EGFR-TKIs.
  • To assess the model's performance against unimodal models and human experts.

Main Methods:

  • Retrospective analysis of clinical and CT imaging data from 222 lung adenocarcinoma patients.
  • Development of the MC-Trans model using Table Transformer and Swin Transformer.
  • Validation on training/validation and two external test cohorts.

Main Results:

  • MC-Trans achieved an ROC-AUC of 0.89 in predicting primary resistance, outperforming unimodal models (tabular: 0.78, CT: 0.63).
  • External validation showed MC-Trans performance comparable to human expert panels.
  • The model also predicted disease progression risk in patients without primary resistance.

Conclusions:

  • MC-Trans demonstrates high utility in predicting primary resistance to third-generation EGFR-TKIs in lung adenocarcinoma.
  • This AI tool can aid clinicians in optimizing therapeutic strategies.
  • MC-Trans offers potential for predicting disease progression beyond primary resistance.

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