Trial reportThoracic cancer2022
A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment.
Trial report in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment.Thoracic cancer · 2022Trial
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Prediction of brain metastasis in patients with epidermal growth factor receptor-positive lung adenocarcinoma based on lung computed tomography-derived radiomics features.BMC medical imaging · 2025Article
- Application of prediction model based on CT radiomics in prognosis of patients with non-small cell lung cancer.BMC cancer · 2025Article
- Radiomics-clinical integration guides prophylactic cranial irradiation decisions in limited-stage small cell lung cancer: a brain metastasis risk stratification model.Translational lung cancer research · 2025Article
- Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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Authors and funding
18 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBrain 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.
methodsA 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.
resultsPatients 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).
conclusionWe 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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