Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy

Background and purpose: Well-performing survival prediction models (SPMs) help patients and health-care professionals to choose treatment aligning with prognosis. This retrospective study aims to investi-gate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algo-rithm (SORG-MLA) for spinal metastases (SM). Materials and methods: From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox -proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs.Results: A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8.Conclusion: Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.(c) 2022 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 175 (2022) 159-166