The application of radiomics is rapidly reshaping the landscape of neuroradiology, shifting the paradigm from traditional qualitative image assessment toward data-driven, quantitative methodologies. This evolution addresses the limitations of subjective radiological interpretations, which can be influenced by interobserver variability and the inherent complexity of neurological structures. By extracting large volumes of imaging features, often imperceptible to the human eye, radiomics facilitates high-throughput, objective characterization of tissue architecture and pathology. Integrating radiomics into neuroradiological practice enables more refined identification and classification of brain and spinal disorders through modalities such as MRI, CT, and nuclear medicine scans. The most recent studies have demonstrated the value of radiomics models in distinguishing tumor subtypes, predicting disease progression, and monitoring treatment response. However, challenges such as the reproducibility of features, harmonization across imaging platforms, and the translation of radiomic signatures into actionable clinical insights remain significant barriers to routine clinical implementation.

This Research Topic aims to advance the field by collecting pioneering research focused on the robust extraction, validation, and clinical application of quantitative radiomics features in neuroradiology. It seeks to address key questions involving the reproducibility of radiomics workflows, the integration of radiomic biomarkers into current diagnostic and prognostic pathways, and the translation of radiomics research into meaningful changes in patient management. There is a strong emphasis on bridging technical innovation with clinical impact, evaluating how radiomics can offer new perspectives in diagnosis, characterization, and personalized management of central nervous system diseases.

To gather further insights on the transition from qualitative to quantitative imaging in neuroradiology, this Research Topic welcomes articles that address, but are not limited to, the following themes:
- Development, validation, and standardization of radiomics algorithms for brain and spine imaging
- Application of radiomics in CT, MRI, Doppler, and nuclear medicine modalities
- Radiomic characterization of specific and challenging pathologies, such as Adenoid Cystic Carcinomas and other tumors of the head, neck, and skull base
- Benchmarking radiomics against conventional qualitative assessments and other quantitative imaging methods
- Integration of radiomic features with clinical, genetic, or functional data for improved diagnosis or prognosis
- Data harmonization, annotation protocols, and challenges in multi-center radiomics studies
- Studies leveraging open-source toolkits, such as Pyradiomics, to promote standardized and reproducible radiomic workflows
- Clinical decision-support tools and workflow optimization based on radiomics outputs
- Case reports and studies demonstrating improved outcomes through radiomics-guided management.

We invite original research, reviews, methods articles, perspectives, and case reports that contribute to this rapidly evolving segment of neuroradiology.

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Radiomics in Neuroradiology: From Qualitative to Quantitative Imaging