External validation of the SORG machine learning algorithms for predicting 90-day and 1-year survival of patients with lung cancer-derived spine metastases: a recent bi-center cohort from China

Abstract

BACKGROUND CONTEXTThe survival prediction of lung cancer-derived spinal metastases is often underestimated by several scores. The SORG machine learning (ML) algorithm is considered a promising tool to predict the risk of 90-day and 1-year mortality in patients with spinal metastases, but not been externally validated for lung cancer. PURPOSEThis study aimed to externally validate the SORG ML algorithms on lung cancer-derived spinal metastases patients from two large-volume, tertiary medical centers between 2018 and 2021. STUDY DESIGN/SETTINGRetrospective, cohort study. PATIENT SAMPLEPatients aged 18 years or older at two tertiary medical centers in China are treated surgically for spinal metastasis. OUTCOME MEASURESMortality within 90 days of surgery, mortality within 1 year of surgery. METHODSThe baseline characteristics were compared between the development cohort and our validation cohort. Discrimination (receiver operating curve), calibration (calibration plot, intercept, and slope), the overall performance (Brier score), and decision curve analysis was used to assess the overall performance of the SORG ML algorithms. RESULTSThis study included 150 patients with lung cancer-derived spinal metastases from two medical centers in China. Ninety-day and 1-year mortality rates were 12.9% (19/147) and 51.3% (60/117), respectively. Lung Cancer with targeted therapies had the lowest Hazard Ratio (HR=0.490), showing an optimal protecting factor. The AUC of the SORG ML algorithm for 90-day mortality prediction in lung cancer-derived spinal metastases is 0.714. While the AUC for 1-year mortality prediction is 0.832 (95CI%, 0.758–0.906). The algorithm for 1-year mortality was well-calibrated with an intercept of 0.13 and a calibration slope of 1.00. However, the 90-day mortality prediction was underestimated with an intercept of 0.60 and a slope of 0.37. The SORG ML algorithms for 1-year mortality showed a greater net benefit than the “treats all or no patients” strategies. CONCLUSIONSIn the latest cohort of lung cancer-derived spinal metastases in China, the SORG algorithms for predicting 1-year mortality performed well on external validation. However, 90-day mortality was underestimated. The algorithm should be further validated by single primary tumor-derived metastasis treated with the latest comprehensive treatment in diverse populations.