Computational Pipeline in Neuroradiomics

Neuroradiomics is a field in neuroimaging that extracts large-scale, quantitative features from central and peripheral nervous systems' images using computational techniques. These features capture intensity distributions, textures, shapes, and spatial patterns. By replacing subjective interpretation with algorithm-driven analysis, neuroradiomics improves objectivity, reproducibility, and consistency, while revealing associations that aid diagnosis, prognosis, and monitoring of neurological and neurosurgical diseases. This chapter presents a step-by-step implementation of the neuroradiomics pipeline, with recommended tools and illustrative Python scripts and command-line snippets provided. The workflow begins with dataset preparation, imaging formats, and computational environments, followed by preprocessing procedures to standardize and normalize neuroimaging data. Segmentation methods are then introduced to define regions of interest, after which radiomic features are extracted using dedicated Python libraries. Subsequent sections demonstrate feature selection, machine learning model development, and performance evaluation, all implemented in reproducible Python environments. Throughout, emphasis is placed on transparency, reproducibility, and practical guidance to support both research and clinical applications. An example implementation is provided to demonstrate practical application and illustrate Python-based workflow implementation with code snippets. By the end of this chapter, readers will have a comprehensive and practical framework for implementing neuroradiomics workflows that would enable them to design, adapt, and apply these methods across diverse neurological applications.