ArticleBMC pulmonary medicine2024
Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study.
Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- Role of radiotherapy on long-term outcomes of patients with small cell lung cancer under different metastasis patterns.World journal of surgical oncology · 2025Article
- A predictive model and mechanistic study of treatment effectiveness in patients newly diagnosed with small cell lung cancer.Frontiers in oncology · 2025Article
- Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network.Diagnostics (Basel, Switzerland) · 2024Article
- Interpretable Machine Learning for Survival Prediction in Small Cell Lung Cancer Patients With Brain Metastases: A Population-Based Study With External Validation.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
6 authors.
Funding
Abstract
backgroundThis study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis.
methodsIn the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA).
resultsIn total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients.
conclusionOur prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.
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