Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas
ObjectivePrimary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.MethodsThe records of 86,763 primary LCs...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1554242/full |
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| author | Bowen Wang Bowen Wang Mengjia Peng Yan Li Jinhang Gao Tao Chang |
| author_facet | Bowen Wang Bowen Wang Mengjia Peng Yan Li Jinhang Gao Tao Chang |
| author_sort | Bowen Wang |
| collection | DOAJ |
| description | ObjectivePrimary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.MethodsThe records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.ResultsThe research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model’s performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.ConclusionsBM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition. |
| format | Article |
| id | doaj-art-c3b18f5488e94560b1a3cd0c3c6c9eaa |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-c3b18f5488e94560b1a3cd0c3c6c9eaa2025-08-20T02:01:05ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-03-011510.3389/fonc.2025.15542421554242Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomasBowen Wang0Bowen Wang1Mengjia Peng2Yan Li3Jinhang Gao4Tao Chang5Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Emergency, General Hospital of Tibet Military Command, Lhasa, ChinaDepartment of Emergency, General Hospital of Tibet Military Command, Lhasa, ChinaPhysical Examination Center, General Hospital of Western Theater Command, Chengdu, ChinaDepartment of Gastroenterology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, ChinaObjectivePrimary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.MethodsThe records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.ResultsThe research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model’s performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.ConclusionsBM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.https://www.frontiersin.org/articles/10.3389/fonc.2025.1554242/fulllung carcinomabrain metastasisprognosismodelimmunology |
| spellingShingle | Bowen Wang Bowen Wang Mengjia Peng Yan Li Jinhang Gao Tao Chang Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas Frontiers in Oncology lung carcinoma brain metastasis prognosis model immunology |
| title | Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| title_full | Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| title_fullStr | Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| title_full_unstemmed | Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| title_short | Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| title_sort | developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas |
| topic | lung carcinoma brain metastasis prognosis model immunology |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1554242/full |
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