The spine is among the most frequent sites of metastatic disease, with an increasing prevalence, leading to increasing rates of surgical interventions. The choice of surgical technique (ranging from minimally invasive techniques, to radical resection), is usually based on expected survival, treatment-related morbidity, and outcome after treatment. A number of scoring systems have been developed to assess the predicted life expectancy of patients with spinal metastasis. Some of the most common predictive scoring systems include: the revised Tokuhashi score, the Tomita score, the modified Bauer score, the revised Katagiri, and the van der Linden scoring system; who are widely used and accepted. While many studies have reviewed the efficacy of historic scoring systems with positive results in many, an increasing amount of studies have shown that the accuracy of historic scoring systems is relatively low, and decreasing over time. The major hypothesis for the decreasing accuracy of these scores is the improved survival associated with new anticancer therapies.

More recently, more modern scoring systems have emerged, such as the SORG nomogram, and machine learning algorithms, as well as open access application for clinicians. Even though a greater number of features is needed in order to establish prognosis, most of the features needed can be found in the patient’s charts. They have been the subject of numerous studies to externally validate their efficacy, and it was shown that that the SORG Nomogram demonstrated the highest accuracy at predicting 30-day and 90-day survival after surgery.

Although emerging scoring systems show promise in predicting survival, none should supplant multidisciplinary consultation when determining the indication for surgical intervention. Survival prediction alone should not serve as the sole justification for surgery, as careful consideration should also be given to the patient’s overall clinical condition, psychological well-being, neurological status, and expected prognosis.