A visualized dynamic prediction model for overall survival in patients diagnosed with brain metastases from lung squamous cell carcinoma

INTRODUCTION: Patients presenting with brain metastases (BMs) from lung squamous cell carcinoma (LUSC) often encounter an extremely poor prognosis. A well‐developed prognostic model would assist physicians in patient counseling and therapeutic decision‐making. METHODS: Patients with LUSC who were diagnosed with BMs between 2000 and 2018 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. Using the multivariate Cox regression approach, significant prognostic factors were identified and integrated. Bootstrap resampling was used to internally validate the model. An evaluation of the performance of the model was conducted by analyzing the area under the curve (AUC) and calibration curve. RESULTS: A total of 1812 eligible patients' clinical data was retrieved from the database. Patients' overall survival (OS) was significantly prognosticated by five clinical parameters. The nomogram achieved satisfactory discrimination capacity, with 3‐, 6‐, and 9‐month AUC values of 0.803, 0.779, and 0.760 in the training cohort and 0.796, 0.769, and 0.743 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. There was also a substantial difference in survival curves between the different prognostic groups stratified by prognostic scores. For ease of access, the model was deployed on a web‐based server. CONCLUSIONS: In this study, a nomogram and a web‐based predictor were developed to assist physicians with personalized clinical decisions and treatment of patients who presented with BMs from LUSC.