Artificial Intelligence and Predictive Modeling in Spinal Oncology: A Narrative Review

Abstract

BACKGROUND: Artificial intelligence (AI) tremendously influences our daily lives and the medical field, changing the scope of medicine. One of the fields where AI, and, in particular, predictive modeling, holds great promise is spinal oncology. An accurate patient prognosis is essential to determine the optimal treatment strategy for patients with spinal metastases. Multiple studies demonstrated that the physician's survival predictions are inaccurate, which resulted in the development of numerous predictive models. However, difficulties arise when trying to interpret these models and, more importantly, assess their quality. OBJECTIVE: To provide an overview of all stages and challenges in developing predictive models using the Skeletal Oncology Research Group machine learning algorithms as an example. METHODS: A narrative review of all relevant articles known to the authors was conducted. RESULTS: Building a predictive model consists of 6 stages: preparation, development, internal validation, presentation, external validation, and implementation. During validation, the following measures are essential to assess the model's performance: calibration, discrimination, decision curve analysis, and the Brier score. The structured methodology in developing, validating, and reporting the model is vital when building predictive models. Two principal guidelines are the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis checklist and the prediction model risk of bias assessment. To date, many predictive modeling studies lack the right validation measures or improperly report their methodology. CONCLUSIONS: A new health care age is being ushered in by the rapid advancement of AI and its applications in spinal oncology. A myriad of predictive models are being developed; however, the subsequent stages, quality of validation, transparent reporting, and implementation still need improvement. CLINICAL RELEVANCE: Given the rapid rise and use of AI prediction models in patient care, it is valuable to know how to assess their quality and to understand how these models influence clinical practice. This article provides guidance on how to approach this.

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