A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging

TL;DR

This review discusses the evolution and application of handcrafted and deep radiomics in neuroimaging, highlighting its potential to improve diagnosis and treatment in neurology while addressing current limitations for clinical adoption.

Contribution

It provides a comprehensive overview of the radiomic pipeline in neuroimaging and compares handcrafted and deep radiomics approaches with practical insights.

Findings

Radiomics can enhance diagnostic accuracy in neurology.

Challenges include standardization and reproducibility of radiomic features.

Collaborative efforts are needed to translate radiomics into clinical practice.

Abstract

Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional review of medical images by clinicians remains subjective, time-consuming, and prone to human error. With the recent availability of medical imaging data, quantification have become important goals in the field. Radiomics, a methodology aimed at extracting quantitative information from imaging data, has emerged as a promising approach to uncover hidden biological information and support decision-making in clinical practice. This paper presents a review of the radiomic pipeline from the clinical neuroimaging perspective, providing a detailed overview of each step with practical advice. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis. Although radiomics shows great potential for increasing diagnostic precision and improving treatment quality in neurology, several limitations hinder its clinical implementation. Addressing these challenges requires collaborative efforts, advancements in image harmonization methods, and the establishment of reproducible and standardized pipelines with transparent reporting. By overcoming these obstacles, radiomics can significantly impact clinical neurology and enhance patient care.