ArticleTranslational lung cancer research2025
Predicting survival in small cell lung cancer patients undergoing various treatments: a machine learning approach.
Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a serum inflammatory biomarker-driven machine-learning model for prognostic stratification in surgical limited-stage small cell lung cancer.Translational lung cancer research · 2025Article
- From Simple Scores to Intelligent Systems: Encouraging the Development, Validation and Adoption of Robust Prognostic Tools in Small Cell Lung Cancer.Technology in cancer research & treatmentReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Small cell lung cancer (SCLC) is highly metastatic, accounting for 1.796 million global cancer-related deaths in 2020, with no established standard care. This study aimed to assess treatment effects on SCLC patient survival across stages and develop a machine learning-based survival prediction tool for accurate overall survival (OS) estimation. Methods: We developed four prediction models: Cox proportional hazard (Cox PH) regression, survival tree (ST), random survival forest (RSF), and gradient boosting survival analysis (GBSA). Patients were randomly split 7:3 into training and test datasets, with 10-fold cross-validation and 50 iterations on the training dataset. Cox PH used hazard ratios, while the other models employed importance values to assess feature predictiveness. Harrell's C-index (C-index) and Brier score (BS) measured model performance, with internal validations using R version 4.2.0. Results: Cox PH outperformed others based on mean C-index and BS. Multivariate analysis across models highlighted distant metastases (M category), tumor stage, and treatment modalities (radiotherapy, chemotherapy, surgery) as key survival predictors. Stratified Cox PH analysis revealed surgery's efficacy in early-stage SCLC (stage II) and radiotherapy's advantage in stage III. Homogeneity was observed in chemotherapy benefits across cancer stages. Conclusions: Surgery, chemotherapy, and radiotherapy are integral in SCLC treatment, contingent on cancer stage and characteristics. Surgery offers promise for early-stage cases, while advanced-stage strategies require further exploration.
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