# Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors—A Narrative Review

## TL;DR

This review explores how radiomics and machine learning can improve spinal tumor diagnosis and treatment, highlighting current methods and future goals.

## Contribution

The paper provides a narrative review of radiomics and ML applications in spinal tumors, suggesting a workflow and future research directions.

## Key findings

- Radiomics and ML show promise in spinal tumor diagnosis and management.
- A suggested workflow for radiomics and ML analysis is outlined for researchers.
- Future work should focus on validating algorithms and improving ethical standards.

## Abstract

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes.