ArticleCancer medicine2026
Real-World Prognostic Analysis of Clinical Characteristics in Extensive-Stage Small Cell Lung Cancer: A Nomogram for Survival Prediction in Patients Receiving First-Line Immunochemotherapy With Serplulimab.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundExtensive-stage small cell lung cancer (ES-SCLC) has limited treatment options, with first-line immunochemotherapy providing modest survival benefits.
objectivesThis single-center, retrospective study aimed to develop a prognostic nomogram using real-world data from ES-SCLC patients treated with serplulimab-based immunochemotherapy to identify key factors influencing overall survival (OS). MATERIALS AND
methods109 ES-SCLC patients treated with first-line immunochemotherapy were analyzed. Baseline clinicopathological characteristics and hematological markers were assessed, and four prognostic factors - liver metastasis, maintenance therapy, albumin level, and neutrophil-to-lymphocyte ratio (NLR) - were identified by LASSO regression. A nomogram was constructed to predict 6-, 12-, and 24-month OS rates. The model's performance was validated with C-index, AUC, calibration curves, and decision curve analysis.
resultsThe nomogram effectively stratified patients into low-, intermediate-, and high-risk groups with significant OS differences (log-rank p < 0.001). Patients with liver metastasis, no maintenance therapy, low albumin levels, and high NLR had worse outcomes. The C-index for the training and internal validation sets was 0.698 and 0.832, respectively, with AUC values exceeding 0.7 across most time points, suggesting favorable discrimination within this exploratory cohort.
conclusionsThe identified nomogram provides an exploratory real-world tool for OS estimation in ES-SCLC patients receiving first-line serplulimab-based immunochemotherapy. By integrating baseline clinical factors and treatment-course information, the model may support dynamic risk stratification and help inform follow-up intensity, supportive care optimization, and prognostic counseling during routine clinical practice. Further prospective, multicenter external validation and time-dependent analyses are warranted to confirm and optimize its clinical applicability.
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