A Novel Prognostication System for Spinal Metastasis Patients Based on Network Science and Correlation Analysis

• Published in: Clinical oncology (Royal College of Radiologists (Great Britain)) 2023-01, Vol.35 (1), p.e20-e29
• Main Authors: Mezei, T., Horváth, A., Nagy, Z., Czigléczki, G., Banczerowski, P., Báskay, J., Pollner, P.
• Format: Article
• Language: English
• Subjects: Humans; Metastatic epidural spinal tumour; personalised therapy; prediction; Prognosis; Retrospective Studies; Risk Assessment; scoring system; Spinal Neoplasms - secondary; survival
• ISSN: 0936-6555; 1433-2981
• DOI: 10.1016/j.clon.2022.09.054

During the progress of oncological diseases, there is an increased probability that spinal metastases may develop, requiring personalised treatment options. Risk calculator systems aim to provide assistance in the therapeutic decision-making process by estimating survival chances. The predictive ability of such calculators can be improved, thereby optimising the choice of personalised therapy. The aim of this research was to create a new risk assessment system and show a method with which other centres can develop their own local score. We created a database by retrospectively processing 454 patients. The prognostic factors were selected via a network science-based correlation analysis that maximises Uno's C-index, keeping only a small number of predictors. To validate the new system, we calculated the D-statistic, the Integrated Discrimination Index, made a five-fold cross-validation and also calculated the integrated time-dependent Brier score. As a result of multivariate Cox analysis, we found five independent prognostic factors suitable for the design of the risk calculator. This new system has a better predictive ability compared with six other well-known systems with an average C-index of 0.706 at 10 years (95% confidence interval 0.679–0.733). An accurate estimation of the life expectancy of cancer patients is essential for the implementation of personalised medicine. The training performance of our system is encouraging, indicating the benefit of a network science-based visualisation step. We believe that in order to further improve the prediction ability, it is necessary to systematise previously ‘unknown’ factors (e.g. radiological morphology). •The spine is the third most commonly affected part of our body by a metastasis.•The knowledge of prognosis is essential for choosing a personalised treatment option.•Demonstration of predictive ability improvement provided by a network visualisation.•Evaluation of time-dependent abilities of the prediction.