克里唑蒂尼
医学
肺癌
间变性淋巴瘤激酶
脑转移
内科学
肿瘤科
队列
比例危险模型
铈替尼
置信区间
临床终点
转移
癌症
临床试验
恶性胸腔积液
作者
Yongluo Jiang,Yixing Wang,Sha Fu,Tao Chen,Yixin Zhou,Xuanye Zhang,Chen Chen,Li-Na He,Wei Du,Haifeng Li,Ziqi Lin,Yuanyuan Zhao,Yunpeng Yang,Hongyun Zhao,Wenfeng Fang,Yan Huang,Shaodong Hong,Li Zhang
标识
DOI:10.1111/1759-7714.14386
摘要
Brain metastasis (BM) comprises the most common reason for crizotinib failure in patients with anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC). We hypothesize that its occurrence could be predicted by a computed tomography (CT)-based radiomics model, therefore, allowing for selection of enriched patient populations for prevention therapies.A total of 75 eligible patients were enrolled from Sun Yat-sen University Cancer Center between June 2014 and September 2019. The primary endpoint was brain metastasis-free survival (BMFS), estimated from the initiation of crizotinib to the date of the occurrence of BM. Patients were randomly divided into two cohorts for model training (n = 51) and validation (n = 24), respectively. A radiomics signature was constructed based on features extracted from chest CT before crizotinib treatment. Clinical model was developed using the Cox proportional hazards model. Log-rank test was performed to describe the difference of BMFS risk.Patients with low radiomics score had significantly longer BMFS than those with higher, both in the training cohort (p = 0.019) and validation cohort (p = 0.048). The nomogram combining smoking history and the radiomics signature showed good performance for the estimation of BMFS, both in the training (concordance index [C-index], 0.762; 95% confidence interval [CI], 0.663-0.861) and validation cohort (C-index, 0.724; 95% CI, 0.601-0.847).We have developed a CT-based radiomics model to predict subsequent BM in patients with non-brain metastatic NSCLC undergoing crizotinib treatment. Selection of an enriched patient population at high BM risk will facilitate the design of clinical trials or strategies to prevent BM.
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