ArticleTranslational cancer research2022
A novel nomogram predicting cancer-specific survival in small cell lung cancer patients with brain metastasis.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development and validation of nomograms to predict brain metastasis-free survival in lung and breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- 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
- 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
- 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
- Noggin contributes to brain metastatic colonization of lung cancer cells.Cancer cell international · 2023Article
- Low radiotherapy dose is suitable for brain metastases in SCLC compared with high dose.Frontiers in oncology · 2023Article
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Abstract
Background: Brain metastasis (BM) is one of the most common metastatic sites in patients with small cell lung cancer (SCLC), and the prognosis remains very poor. This study aimed to establish a novel nomogram for predicting the cancer-specific survival (CSS) in SCLC patients with BM. Methods: SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, which were further used to construct the prognostic nomogram. The discrimination and calibration of nomogram were evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, the area under ROC curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness. Kaplan-Meier survival curve was applied to analyze the survival outcome. Results: A total of 2,462 patients were enrolled in this study, and randomly assigned into training cohort (n=1,723) and validation cohort (n=739). Age, N stage, surgery, radiation, chemotherapy, bone metastasis, liver metastasis and lung metastasis were identified as independent prognostic factors of CSS. The C-indexes of nomogram was 0.683 [95% confidence interval (CI): 0.667-0.699] in the training cohort, and 0.659 (95% CI: 0.634-0.684) in the validation cohort. The AUC values of 6-, 9- and 12-month CSS were 0.723, 0.742 and 0.737 respectively in the training cohort, while 0.715, 0.737 and 0.739 in the validation cohort. The ROC, calibration and DCA curves showed good discrimination, calibration and clinical applicability of this nomogram in predicting prognosis. Moreover, patients in high-risk group had a worse survival outcome than patients in medium-risk and low-risk groups. Conclusions: A novel nomogram was constructed and validated for predicting individual prognosis in SCLC patients with BM. This nomogram could help clinicians make effective treatment strategies for patients.
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