Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas

Abstract

Over recent decades, significant advancements have been made in the treatment and imaging of gliomas. Conventional imaging techniques, such as MRI and CT, play critical roles in glioma diagnosis and treatment but often fail to distinguish between tumor pseudoprogression (Psp) and radiation necrosis (RN) versus true progression (TP). Emerging fields like radiomics and radiogenomics are addressing these challenges by extracting quantitative features from medical images and correlating them with genomic data, respectively. This article will discuss several studies that show how radiomic features (RFs) can aid in better patient stratification and prognosis. Radiogenomics, particularly in predicting biomarkers such as MGMT promoter methylation and 1p/19q codeletion, shows potential in non-invasive diagnostics. Radiomics also offers tools for predicting tumor recurrence (rBT), essential for treatment management. Further research is needed to standardize these methods and integrate them into clinical practice. This review underscores radiomics and radiogenomics' potential to revolutionize glioma management, marking a significant shift towards precision neuro-oncology.

Keywords: glioblastoma; glioma; pseudoprogression; radiation necrosis; radiogenomics; radiomics; tumor progression; tumor recurrence.

Figure 1

A schematic flowchart that summarizes the major findings and clinical implications of radiomics and radiogenomics in the classification, recurrence, recurrence prediction, and response prediction in glioma management. Abbreviations. ML: machine learning, Psp: pseudoprogression, TP: true progression, MGMT: O6-methylguanine-DNA methyltransferase.