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<p>Early prediction of brain metastases (BM) in epidermal growth factor receptor (EGFR) positive lung adenocarcinoma patients is critical for improving treatment strategies and prognosis. This study aimed to enhance BM risk prediction within two years for lung adenocarcinoma patients by using lung CT images and clinical data both derived from initial diagnosis. This study comprised 173 patients with EGFR positive lung adenocarcinoma who underwent diagnostic CT and was stratified into 93 patients with BM and 80 patients without BM. We extracted a total of 1334 radiomic features from each manually delineated primary pulmonary nodule. Least absolute shrinkage and selection operator (LASSO) method was applied to select the optimal image features. Subsequently, the clinical model, radiomic model and hybrid model were constructed employing logistic regression, random forest (RF), support vector machine (SVM), and light gradient boosting machine (LGBM) algorithms separately. Ultimately, the model was evaluated and interpreted utilizing the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and shapley additive explanations (SHAP). The hybrid model consistently exhibited superior predictive performance. Specifically, the logistic regression-based hybrid model exhibited the highest overall performance metrics, with an AUC of 0.94 (95% CI 0.81–0.99). This study demonstrates that the logistic regression-based hybrid model can effectively predict BM in EGFR positive lung adenocarcinoma patients at their initial diagnosis, aiding physicians in developing more accurate treatment plans.</p>

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Early predicting brain metastases of EGFR positive lung adenocarcinoma patients by CT radiomics

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Xinliu He,
  • Chao Guan,
  • Ting Chen,
  • Houde Wu,
  • Liuchao Su,
  • Mingfang Zhao,
  • Li Guo

摘要

Early prediction of brain metastases (BM) in epidermal growth factor receptor (EGFR) positive lung adenocarcinoma patients is critical for improving treatment strategies and prognosis. This study aimed to enhance BM risk prediction within two years for lung adenocarcinoma patients by using lung CT images and clinical data both derived from initial diagnosis. This study comprised 173 patients with EGFR positive lung adenocarcinoma who underwent diagnostic CT and was stratified into 93 patients with BM and 80 patients without BM. We extracted a total of 1334 radiomic features from each manually delineated primary pulmonary nodule. Least absolute shrinkage and selection operator (LASSO) method was applied to select the optimal image features. Subsequently, the clinical model, radiomic model and hybrid model were constructed employing logistic regression, random forest (RF), support vector machine (SVM), and light gradient boosting machine (LGBM) algorithms separately. Ultimately, the model was evaluated and interpreted utilizing the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and shapley additive explanations (SHAP). The hybrid model consistently exhibited superior predictive performance. Specifically, the logistic regression-based hybrid model exhibited the highest overall performance metrics, with an AUC of 0.94 (95% CI 0.81–0.99). This study demonstrates that the logistic regression-based hybrid model can effectively predict BM in EGFR positive lung adenocarcinoma patients at their initial diagnosis, aiding physicians in developing more accurate treatment plans.