Open AccessA CT ‐based radiomics model to predict subsequent brain metastasis in patients with ALK ‐rearranged non–small cell lung cancer undergoing crizotinib treatment
Author(s) -
Jiang Yongluo,
Wang Yixing,
Fu Sha,
Chen Tao,
Zhou Yixin,
Zhang Xuanye,
Chen Chen,
He Lina,
Du Wei,
Li Haifeng,
Lin Zuan,
Zhao Yuanyuan,
Yang Yunpeng,
Zhao Hongyun,
Fang Wenfeng,
Huang Yan,
Hong Shaodong,
Zhang Li
Publication year - 2022
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.14386
Subject(s) - crizotinib , medicine , lung cancer , anaplastic lymphoma kinase , brain metastasis , oncology , cohort , proportional hazards model , ceritinib , confidence interval , clinical endpoint , metastasis , cancer , clinical trial , malignant pleural effusion
Background 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. Methods 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. Results 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). Conclusion 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.