Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
Overview
- The study focuses on the use of radiomics and radiogenomics to diagnose and predict outcomes in gliomas, a type of brain tumor. The hypothesis being tested is whether these techniques can provide more accurate and precise information than traditional methods, such as surgical sampling and radiological findings. The methodology used includes the analysis of MRI data and genomic data to extract and analyze morphological, textural, and functional features, as well as molecular classifications and tumor microenvironment immune infiltration. The primary objective of the study is to evaluate the potential of radiomics and radiogenomics in improving the diagnosis and treatment of gliomas.
Comparative Analysis & Findings
- The study compares the outcomes observed under different experimental conditions, specifically the use of radiomics and radiogenomics versus traditional methods for diagnosing and predicting outcomes in gliomas. The results show that radiomics and radiogenomics provide more accurate and precise information than traditional methods, including the ability to predict histological features, grade, and overall survival in gliomas. The key findings of the study support the hypothesis that radiomics and radiogenomics have the potential to revolutionize the diagnosis and treatment of gliomas.
Implications and Future Directions
- The study's findings have significant implications for the field of research and clinical practice, as they suggest that radiomics and radiogenomics can provide more accurate and precise information than traditional methods for diagnosing and predicting outcomes in gliomas. However, the study also identifies limitations, such as the lack of standardized processes of feature extraction and analysis methodology among different research groups. Future research should focus on developing standardized processes and incorporating machine learning or artificial intelligence to further improve the accuracy and precision of radiomics and radiogenomics in diagnosing and treating gliomas.