Comprehensive Prognostic Model for Immunotherapy in Small Cell Lung Cancer: A Multi-Center Study Integrating Clinical and Blood Biomarkers

QiuQiao Mu¹Yuhao Jing²Yun Ding³Jing-xian Wang⁴Han Zhang²Yuhang Jiang²Tan Lin⁵Jie Zhang²Xin Li^6*Daqiang Sun^6*

• ¹Tianjin Medical University, Tianjin, China
• ²Tianjin University, Tianjin, China
• ³Fujian Provincial Hospital, Fuzhou, China
• ⁴Yantai Yuhuangding Hospital, Yantai, China
• ⁵Qingdao Municipal Hospital Group, Qingdao, China
• ⁶Tianjin Chest Hospital, Tianjin, China

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.