MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis

TL;DR

This paper reviews how MRI radiomics combined with machine learning can improve non-invasive prediction of IDH mutation status in glioblastoma, aiding diagnosis and personalized treatment planning.

Contribution

It provides a comprehensive overview of recent advances in MRI radiomics for IDH genotype prediction in glioblastoma, highlighting methodological developments and clinical implications.

Findings

Radiomics enhances non-invasive IDH mutation detection.

Machine learning models improve diagnostic accuracy.

Standardized features reduce radiologist bias.

Abstract

Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application, particularly in oncology because many of the important oncological biomarkers are not visible to the naked eye. The recent advent of big data, including in medical imaging, and the development of new ML techniques brought the possibility of faster and more accurate oncological diagnosis. Furthermore, standardised mathematical feature extraction based on radiomics helps to eliminate possible radiologist bias. This paper reviews the recent development in the oncological use of MRI radiomic features. It focuses on the identification of the isocitrate dehydrogenase (IDH) mutation status, which is an important biomarker for the diagnosis of glioblastoma and grade IV astrocytoma.