Comparative Evaluation of Prognostic Scoring Systems for Metastatic Spinal Tumors: Utility of the New Katagiri and Skeletal Oncology Research Group Models
- 1Department of Orthopaedic Surgery, Tokyo Metropolitan Komagome Hospital, Bunkyo 113-8677, Tokyo, Japan
- 2Department of Orthopaedic Surgery, The University of Tokyo Hospital, Bunkyo 113-8655, Tokyo, Japan
- 3Department of Musculoskeletal Oncology, Tokyo Metropolitan Komagome Hospital, Bunkyo 113-8677, Tokyo, Japan
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). Results: For 90-day survival, AUCs were 0.77 (95% confidence interval [CI], 0.58–0.95) for the SORG MLA and 0.76 (95% CI, 0.60–0.91) for the New Katagiri score. For 1-year survival, AUCs were 0.79 (95% CI, 0.72–0.86) for the New Katagiri score, 0.76 (95% CI, 0.68–0.83) for the SORG classic scoring algorithm, and 0.74 (95% CI, 0.66–0.82) for the SORG MLA. After Holm adjustment, no significant pairwise differences were found among these three models at either prediction horizon. SORG MLA calibration deviated from perfect calibration. Conclusions: In this single-center surgical cohort, the New Katagiri score and SORG models showed higher discrimination than several conventional scoring systems. However, these findings do not establish model equivalence or the clinical benefit of model-guided decisions. Broader external validation and assessment of clinical utility are needed.
Related
- Prevalence and Prognostic Significance of Positron Emission Tomography–Computed Tomography–Defined Low Skeletal Muscle Mass (Radiological Sarcopenia) in Metastatic Breast Cancer
- Proactive Prediction of Alarms in Hemodialysis Machines: A Comparative Evaluation of AI Models for Clinical Prognostic Support
- Evaluation of clinical outcomes and prognostic factors in patients with metastatic appendiceal adenocarcinoma treated with first-line chemotherapy: A multi-institutional retrospective study
- Comparative analysis of PSMA-RADS Version 2.0 versus bone uptake metastatic probability (BUMP) score for characterising focal bone uptake on [18F]PSMA-1007 PET/CT in prostate cancer – a multicentre evaluation
- Prognostic Modeling of 1-Year Survival in Hepatocellular Carcinoma: A Comparative Evaluation of Machine Learning Methods with Class Balancing.
BibTeX
@article{miyahara2026comparative,
title = {{Comparative Evaluation of Prognostic Scoring Systems for Metastatic Spinal Tumors: Utility of the New Katagiri and Skeletal Oncology Research Group Models}},
author = {Junya Miyahara and Kazuki Abe and Rie Kodama and Masanori Fujiwara and Takuma Koyama and Takahiro Hozumi and Toshihide Hirai and Shurei Sugita},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207780},
url = {https://www.mdpi.com/2077-0383/15/20/7780},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Comparative Evaluation of Prognostic Scoring Systems for Metastatic Spinal Tumors: Utility of the New Katagiri and Skeletal Oncology Research Group Models
AU - Junya Miyahara
AU - Kazuki Abe
AU - Rie Kodama
AU - Masanori Fujiwara
AU - Takuma Koyama
AU - Takahiro Hozumi
AU - Toshihide Hirai
AU - Shurei Sugita
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207780
UR - https://www.mdpi.com/2077-0383/15/20/7780
AB - 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). Results: For 90-day survival, AUCs were 0.77 (95% confidence interval [CI], 0.58–0.95) for the SORG MLA and 0.76 (95% CI, 0.60–0.91) for the New Katagiri score. For 1-year survival, AUCs were 0.79 (95% CI, 0.72–0.86) for the New Katagiri score, 0.76 (95% CI, 0.68–0.83) for the SORG classic scoring algorithm, and 0.74 (95% CI, 0.66–0.82) for the SORG MLA. After Holm adjustment, no significant pairwise differences were found among these three models at either prediction horizon. SORG MLA calibration deviated from perfect calibration. Conclusions: In this single-center surgical cohort, the New Katagiri score and SORG models showed higher discrimination than several conventional scoring systems. However, these findings do not establish model equivalence or the clinical benefit of model-guided decisions. Broader external validation and assessment of clinical utility are needed.
SN - 2077-0383
ER -