# Are the SORG and OPTImodel, Tokuhashi and Tomita Algorithms Still Suitable as Predictors of Survival in Patients With Vertebral Metastases in Routine Clinical Practice?

## TL;DR

This study compares the accuracy of several survival prediction models for patients with vertebral metastases and finds that newer models do not outperform older ones.

## Contribution

The study evaluates the performance of traditional and newer survival prediction models in a real-world clinical setting for vertebral metastases.

## Key findings

- Tokuhashi had the highest accuracy for survival prediction under 6 months (77.5%).
- OPTImodel showed the best accuracy for survival prediction over 1 year (90.8%).
- Newer models like SORG ML and OPTImodel did not outperform traditional models like Tokuhashi and Tomita.

## Abstract

To evaluate the performance of the Tokuhashi, Tomita, SORG machine learning (SORG ML), and OPTImodel algorithms as survival predictors for vertebral metastases in clinical practice.

A retrospective study (2013–2023) analyzed 573 patients from Cabueñes University Hospital (Asturias, Spain). Thirty‐two demographic, epidemiological, clinical, and analytical variables were considered, including diagnosis chronology and survival.

Among the 573 patients studied, 272 (47.4%) presented visceral metastases at the time of diagnosis. A total of 362 patients (63.2%) had associated comorbidities. The most frequent primary histological diagnoses in these patients were lung 147 (25.7%), prostate 146 (25.5%), breast 118 (20.6%), kidney 30 (5.2%), and colorectal 29 (5.1%). The median survival of the cohort was 185 days. The accuracy rates for the Tokuhashi, SORG ML, OPTImodel, and Tomita algorithms were 0.5509, 0.4812, 0.3404, and 0.3858, respectively. The models with the highest accuracy rates in specific time segments were Tokuhashi (77.5% for < 6 months) and OPTImodel (90.8% for more than 1 year). The areas under the curve (AUC) for survival intervals were as follows: Tokuhashi at 42 days (73.19%), 90 days (79.3%), and 365 days (82.73%); Tomita at 42 days (69.27%), 90 days (76.82%), and 365 days (78.79%); SORG ML at 42 days (52.77%), 90 days (51.69%), and 365 days (51.38%).

All models showed relatively low accuracy. The newer models (OPTImodel, SORG ML) did not outperform the traditional Tomita and Tokuhashi in predicting survival for vertebral metastases patients.

The study evaluated survival prediction algorithms for vertebral metastases, comparing Tokuhashi, Tomita, SORG ML, and OPTImodel. Tokuhashi showed the highest accuracy for < 6 months (77.5%) and OPTImodel for > 1 year (90.8%). Overall, newer models (SORG ML, OPTImodel) did not outperform classic methods, highlighting limitations in predicting survival in clinical practice.