医学
内科学
列线图
肺癌
肿瘤科
磁共振成像
单变量分析
表皮生长因子受体
逻辑回归
间变性淋巴瘤激酶
多元分析
放射科
癌症
恶性胸腔积液
作者
Jun Qu,Tao Zhang,X.-C. Zhang,Wen Zhang,Y. Li,Qiyong Gong,Lie Yao,Su Lui
标识
DOI:10.1016/j.crad.2024.01.005
摘要
AIM
To identify clinical and magnetic resonance imaging (MRI) radiomics predictors specialised for intracranial progression (IP) after first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment in non-small-cell lung cancer (NSCLC) patients with brain metastases (BMs). MATERIALS AND METHODS
Seventy EGFR-mutated NSCLC patients with a total of 212 BMs who received first-line EGFR-TKI therapy were enrolled. Radiomics features were extracted from the BM regions on the pretreatment contrast-enhanced T1-weighted images, and the radiomics score (rad-score) of each BM was established based on the selected features. Furthermore, the mean rad-score derived from the average rad-score of all included BMs in each patient was calculated. Univariate and multivariate logistic regression analyses were performed to identify potential predictors of IP. Prediction models based on different predictors and their combinations were constructed, and nomogram based on the optimal prediction model was evaluated. RESULTS
Thirty-three (47.1 %) patients developed IP, and the remaining 37 (52.9 %) patients were IP-free. EGFR-19del mutation (OR 0.19, 95 % CI 0.05–0.69), third-generation TKI treatment (OR 0.33, 95 % CI 0.16–0.67) and mean rad-score (OR 5.71, 95 % CI 1.65–19.68) were found to be independent predictive factors. Models based on these three predictors alone and in combination (combined model) achieved AUCs of 0.64, 0.64, 0.74, and 0.86 and 0.64, 0.64, 0.75, and 0.84 in the training and validation sets, respectively, and the combined model demonstrated optimal performance for predicting IP. CONCLUSIONS
The model integrating EGFR-19del mutation, third-generation TKI treatment and mean rad-score had good predictive value for IP after EGFR-TKI treatment in NSCLC patients with BM.
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