ArticleBMC cancer2021
A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain Metastases Status and Immunotherapy Efficacy in Advanced Lung Cancer: A Systematic Review and Meta-Analysis.Frontiers in immunology · 2021Pooled it
- A prognostic nomogram to predict survival in elderly patients with small-cell lung cancer: a large population-based cohort study and external validation.BMC cancer · 2022Trial
- Development and external validation of an XGBoost-based prognostic model for small cell lung cancer with brain metastases.Translational cancer research · 2026Article
- Prognostic value of pretherapy CT-based Node-RADS for overall survival in extensive-stage small cell lung cancer treated with chemoimmunotherapy: a comparison with clinical N staging.Quantitative imaging in medicine and surgery · 2026Article
- A nomogram predicting prognosis of extensive-stage small cell lung cancer patients receiving chemoradiotherapy.Radiation oncology (London, England) · 2025Article
- Application value and safety of trilaciclib in first-line chemotherapy combined with immunotherapy for extensive-stage small cell lung cancer: A real-world study.Oncology letters · 2025Article
- Survival Benefit of Temozolomide Plus Irinotecan as Second-Line Therapy in Small Cell Lung Cancer: A Retrospective Single-Center Study.Journal of clinical medicine · 2025Article
- Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas.Frontiers in oncology · 2025Article
- Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.Scientific reports · 2024Article
- Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis.Cancer causes & control : CCC · 2024Article
- Prognostic factors in extensive-stage small cell lung cancer patients with organ-specific metastasis: unveiling commonalities and disparities.Journal of cancer research and clinical oncology · 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
- Development and validation of nomograms for predicting survival in small cell lung cancer patients with brain metastases: a SEER population-based analysis.American journal of translational research · 2024Article
- Prognostic factors in clinical stage IIIA small cell lung cancer: An analysis of a population-based cancer registry in Taiwan.PloS one · 2024Article
- A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma.Neuro-oncology · 2023Article
- Identification of a visualized web-based nomogram for overall survival prediction in patients with limited stage small cell lung cancer.Scientific reports · 2023Article
- A nomogram to predict the cumulative risk for brain metastases in patients with limited-stage small cell lung cancer without prophylactic cranial irradiation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
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- Development and validation of a nomogram for the prediction of brain metastases in small cell lung cancer.The clinical respiratory journal · 2023Article
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7 authors.
Funding
Abstract
backgroundThe prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.
methodsThe 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.
resultsUnivariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.
conclusionsWe established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.
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