A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment
Abstract 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...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Thoracic Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1759-7714.14386 |
| _version_ | 1818534490951122944 |
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| author | Yongluo Jiang Yixing Wang Sha Fu Tao Chen Yixin Zhou Xuanye Zhang Chen Chen Li‐na He Wei Du Haifeng Li Zuan Lin Yuanyuan Zhao Yunpeng Yang Hongyun Zhao Wenfeng Fang Yan Huang Shaodong Hong Li Zhang |
| author_facet | Yongluo Jiang Yixing Wang Sha Fu Tao Chen Yixin Zhou Xuanye Zhang Chen Chen Li‐na He Wei Du Haifeng Li Zuan Lin Yuanyuan Zhao Yunpeng Yang Hongyun Zhao Wenfeng Fang Yan Huang Shaodong Hong Li Zhang |
| author_sort | Yongluo Jiang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T18:12:19Z |
| format | Article |
| id | doaj.art-1813850a98a94b6db579f9e23f06634c |
| institution | Directory Open Access Journal |
| issn | 1759-7706 1759-7714 |
| language | English |
| last_indexed | 2024-12-11T18:12:19Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj.art-1813850a98a94b6db579f9e23f06634c2022-12-22T00:55:31ZengWileyThoracic Cancer1759-77061759-77142022-06-0113111558156910.1111/1759-7714.14386A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatmentYongluo Jiang0Yixing Wang1Sha Fu2Tao Chen3Yixin Zhou4Xuanye Zhang5Chen Chen6Li‐na He7Wei Du8Haifeng Li9Zuan Lin10Yuanyuan Zhao11Yunpeng Yang12Hongyun Zhao13Wenfeng Fang14Yan Huang15Shaodong Hong16Li Zhang17State Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaCellular & Molecular Diagnostics Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat‐sen Memorial Hospital, Sun Yat‐sen University Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaState Key Laboratory of Oncology in South China Guangzhou ChinaAbstract 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.https://doi.org/10.1111/1759-7714.14386ALK‐positiveimage biomarkerslung cancerresponse predictiontargeted therapy |
| spellingShingle | Yongluo Jiang Yixing Wang Sha Fu Tao Chen Yixin Zhou Xuanye Zhang Chen Chen Li‐na He Wei Du Haifeng Li Zuan Lin Yuanyuan Zhao Yunpeng Yang Hongyun Zhao Wenfeng Fang Yan Huang Shaodong Hong Li Zhang 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 ALK‐positive image biomarkers lung cancer response prediction targeted therapy |
| title | A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment |
| title_full | A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment |
| title_fullStr | A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment |
| title_full_unstemmed | A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment |
| title_short | A CT‐based radiomics model to predict subsequent brain metastasis in patients with ALK‐rearranged non–small cell lung cancer undergoing crizotinib treatment |
| title_sort | ct based radiomics model to predict subsequent brain metastasis in patients with alk rearranged non small cell lung cancer undergoing crizotinib treatment |
| topic | ALK‐positive image biomarkers lung cancer response prediction targeted therapy |
| url | https://doi.org/10.1111/1759-7714.14386 |
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