| 张硕,梁小红,卢洁.基于非小细胞肺癌胸部CT表现联合表皮生长因子受体突变状态预测其同步与异时脑转移[J].中国医学影像技术,2026,42(5):672~676 |
| 基于非小细胞肺癌胸部CT表现联合表皮生长因子受体突变状态预测其同步与异时脑转移 |
| Chest CT manifestations of non-small cell lung cancer combined with epidermal growth factor receptor mutation status for predicting synchronous or metachronous brain metastasis |
| 投稿时间：2026-04-22 修订日期：2026-05-14 |
| DOI：10.13929/j.issn.1003-3289.2026.05.004 |
| 中文关键词: 癌,非小细胞肺 肿瘤转移 脑肿瘤 体层摄影术,X线计算机 表皮生长因子受体 |
| 英文关键词:carcinoma, non-small-cell lung neoplasm metastasis brain neoplasms tomography, X-ray computed epidermal growth factor receptor |
| 基金项目:首都医科大学宣武医院"汇智"人才工程学者计划(HZ2021ZCLJ005)。 |
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| 中文摘要: |
| 目的 观察基于非小细胞肺癌(NSCLC)胸部CT表现联合表皮生长因子受体(EGFR)突变状态预测其同步(SBM)与异时脑转移(MBM)的价值。方法 回顾性纳入151例NSCLC脑转移患者,根据病理确诊NSCLC至首次检出脑转移的时间间隔划分SBM组(＜2个月,n=81)与MBM组(≥2个月,n=70);采用单因素和多因素logistic回归分析筛选SBM与MBM的独立危险因素,以受试者工作特征曲线下面积(AUC)评估其预测效能。结果 EGFR突变阳性、毛刺征、血管集束征及高N分期均为SBM的独立危险因素(P均＜0.05),而空气支气管征为MBM的独立危险因素(P＜0.05)。联合EGFR突变状态、毛刺征、血管集束征、空气支气管征及N分期预测NSCLC SBM的AUC为0.892,高于各单一因素(AUC分别为0.688、0.706、0.667、0.725及0.693,Z=5.341~6.154,P均＜0.001)。结论 基于NSCLC胸部CT表现联合EGFR突变状态可有效预测其 SBM与MBM。 |
| 英文摘要: |
| Objective To investigate the value of chest CT manifestations of non-small cell lung cancer (NSCLC) combined with epidermal growth factor receptor (EGFR) mutation status for predicting synchronous brain metastasis (SBM) or metachronous brain metastasis (MBM). Methods A total of 151 NSCLC patients with brain metastases were retrospectively enrolled and divided into SBM group (＜2 months, n=81) and MBM group (≥2 months, n=70) according to the interval between pathological diagnosis of NSCLC and the first detection of brain metastasis. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors of SBM and MBM, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance of the above factors. Results EGFR mutation positivity, spiculation sign, vascular convergence sign and higher N stage were all independent risk factors of SBM (all P＜0.05), while air bronchogram sign was an independent risk factor of MBM (P＜0.05). The AUC of the combination of EGFR mutation status, spiculation sign, vascular convergence sign, air bronchogram sign and N stage for predicting SBM of NSCLC was 0.892, higher than that of each factor alone (AUC=0.688, 0.706, 0.667, 0.725 and 0.693, respectively; Z=5.341—6.154, all P＜0.001). Conclusion Chest CT features of NSCLC combined with EGFR mutation status could be used to effectively predict SBM and MBM. |
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