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Objectives <p>To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with EGFR-mutant non-small cell lung cancer (NSCLC) brain metastases (BMs).</p> Methods <p>This study retrospectively analyzed data from 338 patients with EGFR-mutant NSCLC BMs from three centers, including MRI, clinical and pathological data, and radiological features. Based on the selected significant radiomic features from intratumoral regions extracted from CE-T1WI, while exploring the value of features in 3/5/8&#xa0;mm peritumoral regions, seven commonly used machine learning algorithms were compared to select the optimal one for model construction, and the best algorithm was selected for model construction. In the model predicting 1-year therapeutic efficacy, clinical, radiomic, and combined models were constructed separately. The model performance was evaluated using receiver operating characteristic curves.</p> Results <p>The final development cohort comprised 285 patients from Center 1, while the external validation set included 57 patients from Centers 2 and 3. In the model predicting 1-year EGFR-TKIs efficacy, the random forest algorithm, which showed the best application, was used to construct the model. Compared with the radiomic and clinical models, the combined model exhibited superior area under the curve performance in the test set (0.756 vs. 0.644 vs. 0.668). In the external validation set, the combined model achieved an area under the curve of 0.743 (95% CI: 0.604–0.881).</p> Conclusion <p>Compared to single clinical or radiomic models, the combined model was more effective in predicting the 1-year efficacy of EGFR-TKIs in patients with NSCLC BMs with EGFR mutations.</p>

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Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Lian-Yu Sui,
  • Tian-Ye Zhang,
  • Cheng Cheng,
  • Li-Hong Xing,
  • Huan Meng,
  • Chong Liu,
  • Qi Wang,
  • Jia-Ning Wang,
  • Tian-Shuo Zhang,
  • Kun Liu,
  • Xiao-Ping Yin

摘要

Objectives

To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with EGFR-mutant non-small cell lung cancer (NSCLC) brain metastases (BMs).

Methods

This study retrospectively analyzed data from 338 patients with EGFR-mutant NSCLC BMs from three centers, including MRI, clinical and pathological data, and radiological features. Based on the selected significant radiomic features from intratumoral regions extracted from CE-T1WI, while exploring the value of features in 3/5/8 mm peritumoral regions, seven commonly used machine learning algorithms were compared to select the optimal one for model construction, and the best algorithm was selected for model construction. In the model predicting 1-year therapeutic efficacy, clinical, radiomic, and combined models were constructed separately. The model performance was evaluated using receiver operating characteristic curves.

Results

The final development cohort comprised 285 patients from Center 1, while the external validation set included 57 patients from Centers 2 and 3. In the model predicting 1-year EGFR-TKIs efficacy, the random forest algorithm, which showed the best application, was used to construct the model. Compared with the radiomic and clinical models, the combined model exhibited superior area under the curve performance in the test set (0.756 vs. 0.644 vs. 0.668). In the external validation set, the combined model achieved an area under the curve of 0.743 (95% CI: 0.604–0.881).

Conclusion

Compared to single clinical or radiomic models, the combined model was more effective in predicting the 1-year efficacy of EGFR-TKIs in patients with NSCLC BMs with EGFR mutations.