Abstract
Study DesignNarrative review.ObjectivesBack pain is one of the leading causes of disability worldwide. While conventional imaging interpretation remains subjective and expertise-dependent, radiomics offers quantitative, data-driven analysis of medical images. We aimed to evaluate the current literature for the application of radiomics in: (1) soft tissue characterization, (2) hard tissue analysis, and (3) treatment outcome prediction in back pain conditions.MethodsWe conducted a PRISMA-style literature search across PubMed, Google Scholar, Wiley, Springer, and IEEE Xplore, focusing on studies from the past 4 years. From 296 identified articles, 22 met inclusion criteria based on their use of radiomic methods and association with pain outcomes.ResultsCurrent literature demonstrates that in many, but not all cases, using radiomics improves clinical models for soft and hard tissue diagnostics as well as for prognosis and treatment prediction. However, the improvements can be minor. There also exist limitations that prevent widespread clinical adoption of radiomics, including a lack of standardization in image acquisition/analysis protocols, homogeneity of patient populations studied, and inadequate integration with existing clinical imaging systems. Additionally, much current work is based on retrospective data instead of real-world data, where there is often an added complexity. Yet, there is increasing work in developing combined models where clinical features, demographics, and patient history are used to enhance the output and accuracy of radiomics.ConclusionsRadiomics can improve back pain diagnosis and treatment. Future directions should focus on developing generalizable radiomics models applicable to broad patient populations, imaging systems, and clinician-interpretable interfaces.