ArticleLung2022
Risk Factors, Prognosis, and a New Nomogram for Predicting Cancer-Specific Survival Among Lung Cancer Patients with Brain Metastasis: A Retrospective Study Based on SEER.
Article in Lung, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 15 citations in OpenAlex.
- Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database.BMC pulmonary medicine · 2023Trial
- Machine learning models based on XGBoost algorithm to predict prognosis of lung cancer brain metastases.Journal of thoracic disease · 2026Article
- [Research Progress on Circulating Tumor DNA Status and Postoperative Central Nervous System Recurrence Risk and Overall Survival in Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Development of machine learning-based prognostic models for small cell lung cancer with brain metastases: an analysis of SEER and Chinese populations.Journal of thoracic disease · 2025Article
- Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma.Scientific reports · 2025Article
- Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning.Discover oncology · 2025Article
- Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas.Frontiers in oncology · 2025Article
- Risk factors and a new nomogram for glioblastoma: based on a retrospective study.Frontiers in immunology · 2025Article
- Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.Scientific reports · 2024Article
- Machine learning-based prognostic models and factors influencing the benefit of surgery on primary lesion for patients with lung cancer brain metastases.American journal of cancer research · 2024Article
- Article
- A visualized dynamic prediction model for overall survival in patients diagnosed with brain metastases from lung squamous cell carcinoma.The clinical respiratory journal · 2023Article
- LUNG Year in Review: 2022.Lung · 2023Article
- Risk factors and a new nomogram for predicting brain metastasis from lung cancer: a retrospective study.Frontiers in oncology · 2023Article
- Slug Mediates MRP2 Expression in Non-Small Cell Lung Cancer Cells.Biomolecules · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo make a comprehensive population-based study on risk and prognostic factors of brain metastasis from lung cancer.
methodsA total of 91,643 patients diagnosed with lung cancer from 2010 to 2018 were collected from the Surveillance, Epidemiology and End results (SEER) database. To analyze the risk and prognostic factors of brain metastasis among lung cancer patients, both Logistic and Cox regression methods were applied, respectively. Also, the competing risk regression model was performed to establish a new nomogram to predict cancer-specific survival (CSS).
resultsAmong the 91,643 lung cancer patients, 10,855 were found to have brain metastasis, with the incidence of 11.84%. The residence, age, race, income, primary site, histological type, extracranial metastasis, T stage, and N stage were all found to be independent risk factors of brain metastasis. The median overall survival (OS) of brain metastasis patients was limited to 6.08 months. By dividing patients randomly into a primary cohort with 7237 patients and a validation cohort with 3618 patients, a conclusion that the income, race, gender, age, histological type, extracranial metastasis, T stage, and N stage were all associated with the prognosis of brain metastasis was drawn. Our established primary-cohort-based new nomogram showed a good discriminative ability in predicting the probability of CSS among patients with brain metastasis, and the C-index was 0.62. Besides, the calibration curves for CSS also showed that the predicted survival by nomogram was consistent with the actual survival in the validation cohort.
conclusionOur study shall provide a deeper insight into the risk factors and prognosis of brain metastasis among lung cancer patients.
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