Journal of Clinical Medicine
Comparative Evaluation of Prognostic Scoring Systems for Metastatic Spinal Tumors: Utility of the New Katagiri and Skeletal Oncology Research Group Models
By Junya Miyahara, Kazuki Abe, Rie Kodama and 5 others.
Abstract
Background
Prognostic models for metastatic spinal tumors have evolved from conventional point-based scores to models incorporating treatment, laboratory, and machine-learning variables. However, comparisons in contemporary Japanese surgical cohorts remain limited.
Methods
This retrospective study included 171 of 208 consecutive patients who underwent surgery for metastatic spinal tumors between January 2017 and August 2024 and had ascertainable 1-year survival status. Seven prognostic systems were compared using receiver operating characteristic area under the curve (AUC) analysis for 90-day and 1-year survival. Pairwise AUC comparisons were adjusted using the Holm method. Calibration and Brier scores were also assessed for the Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA).