Integration of clinical and blood parameters in risk prognostication for patients receiving immunochemotherapy for extensive stage small cell lung cancer: real-world data from two centers.

BACKGROUND

Immune checkpoint inhibitors (ICIs) had modest advances in the treatment of extensive-stage small cell lung cancer (ES-SCLC) in clinical trials, but there is a lack of biomarkers for prognosis in clinical practice.

METHODS

We retrospectively collected data from ES-SCLC patients who received ICIs combined chemotherapy from two centers in China, integrated clinical and blood parameters, and constructed risk prognostication for immunochemotherapy. The population was divided into high- and low-risk groups, and the performance of the model was assessed separately in the training and validation cohorts.

RESULTS

Two hundred and twenty and 43 patients were included in the training and validation groups, respectively. The important predictors were screened including body mass index, liver metastases, coefficient variation of red blood cell distribution width, lactate dehydrogenase, albumin, and C-reactive protein. Predicting 1-year overall survival (OS), the AUC values under ROC for the model under training, internal validation, and external validation were 0.760, 0.732, and 0.722, respectively, and the calibration curve and clinical decision curve performed well. Applied the model to divide patients into low-risk and high-risk groups, and the median OS was 23.7 months and 9.1 months, and the median progression-free survival was 8.2 months and 4.8 months, respectively; furthermore, this ability to discriminate survival was also observed in the validation cohort.

CONCLUSIONS

We constructed a novel prognostic model for ES-SCLC to predict survival employing baseline tumor burden, nutritional and inflammatory parameters, it is easily measured to screen high-risk patient populations.