ArticleFrontiers in oncology2025
Comprehensive prognostic model for immunotherapy in small cell lung cancer: a multi-center study integrating clinical and blood biomarkers.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Role of thoracic radiotherapy for extensive-stage small cell lung cancer in the era of immunotherapy: a review of current evidence.Frontiers in immunology · 2026Review
- Prognostic impact of baseline blood biomarkers in SCLC first-line treatment.Therapeutic advances in medical oncology · 2026Article
- Prognostic value of the MDACC-NLR score in extensive-stage small-cell lung cancer treated with first-line chemoimmunotherapy.Frontiers in immunology · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Small cell lung cancer (SCLC) is a highly aggressive and rapidly progressing form of lung cancer that is difficult to treat. Immunotherapy has provided encouraging outcomes, but only a small proportion of patients experience significant benefit. Predicting which patients will respond to immunotherapy is essential for maximizing treatment effectiveness. Methods: This retrospective analysis included 319 SCLC patients from multiple centers in China who underwent immune checkpoint inhibitor (ICI) therapy. Clinical features and peripheral blood biomarkers were used together to create a prediction system. This system aims to forecast overall survival (OS) and progression-free survival (PFS). Univariate and multivariate Cox regression analyses were used to identify prognostic factors. A nomogram was then constructed to perform risk stratification. The model's performance was evaluated using multiple methods. Time-dependent ROC analysis was applied to assess its predictive accuracy. Decision curve analysis (DCA) was used to determine its clinical utility. Additionally, calibration plots were created to examine the model's consistency with actual outcomes. Results: In SCLC patients, age, brain metastasis, cigarettes per day, lnNSE (Natural Logarithm of Neuron-Specific Enolase), lnAISI (Natural Logarithm of the Aggregate Immune-Inflammatory Index), and lnCLR (Natural Logarithm of the CRP-to-Albumin Ratio) were found to be key factors affecting OS. A nomogram incorporating six variables exhibited excellent discrimination, calibration, and practical utility in both training and validation cohorts. Notably, lnAISI and lnCLR, indicators of systemic immune-inflammation, showed significant predictive value. Conclusion: This study developed a convenient and effective multi-factor survival prediction model based on clinical and hematological markers. The model provides a tool for personalized management of immunotherapy in SCLC patients. It offers new insights and practical evidence for precision treatment in SCLC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.