Introduction: Currently, prediction of survival for patients with cancer is by TNM staging. Identifying accurate prognostic markers of survival would allow better treatment stratification between more aggressive treatment strategies or palliation. This is especially relevant for patients with spinal metastases who all have identical TNM staging, and whose surgical decision-making is potentially complex. Analytic morphometrics quantifies patient frailty by measuring lean muscle mass and can predict risk for postoperative morbidity after lumbar spine surgery.1 This study evaluates whether morphometrics is predictive of survival in patients with spinal metastasis.

Methods: Utilizing a retrospective registry of spinal metastases patients who have undergone stereotactic body radiation therapy (SBRT), we identified patients with primary lung cancer. Morphometric measurements were taken of the psoas using CT of the lumbar spine at the time of SBRT. Patients were stratified into tertiles based on psoas muscle area. The primary outcome measure was overall survival from the date of CT scan. Cox proportional hazards regression analyses were done to estimate the hazard ratios.

Results: A total of 168 patients were identified, with 54% male and 54% with multiple level metastases. The median survival for all patients was 185.5 days (95%CI=146-228 days). Patients in the smallest third for left psoas size had significantly shorter survival: 139 days versus 222 days, hazard ratio 1.47 (95%CI=1.06-2.04), p=0.007. Patients below the median psoas size also had significantly shorter survival: 146 days versus to 253.5 days, hazard ratio 1.43 (95%CI=1.05-1.94), p=0.025. Right psoas size was not predictive of mortality. Survival was not associated with gender, nor the number of levels of metastasis.

Conclusions: In patients with lung cancer metastases to the spine, morphometric analysis of psoas muscle size can be used to identify patients who are at risk for shorter survival. Further research is needed to see if these methodologies can be applied to other cancer histologies.

Patient Care: My research will improve patient care because it defines a way to objectively predict which patients will pass more quickly from cancer metastases.

Learning Objectives: Learning Objectives: By the conclusion of this session, participants should be able to: 1) Describe why it is important to predict outcomes in patients with spinal metastases 2) Discuss the role of morphometrics in predicting mortality in patients with spinal metastases 3) Apply morphometrics to stratify high risk and low risk patients for spinal surgery

References: 1. Zakaria HM, Schultz L, Mossa-Basha F, Griffith B, Chang V. Morphometrics as a predictor of perioperative morbidity after lumbar spine surgery. Neurosurg Focus. Oct 2015;39(4):E5.