Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.

M Ak,S.A Toll,S Khatua,K.Z Hein,R.R Colen

doi:10.3174/ajnr.a7297

Abstract

Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

Highlights

It is an essential process of mapping intensities to a standard reference scale in MR imaging to account for variations between patients and longitudinal studies and to increase radiomics reproducibility
Recent studies have shown that radiomics models can preoperatively predict O6methylguanine-DNA methyltransferase (MGMT) methylation, epidermal growth factor (EGFR) amplification, and EGFR variant III status in GBM.[48,58,59,60]
The evolving and molecular profile of lower-grade gliomas, including the favorable outcome associated with IDH1 and 1p/19q codeletion mutation status, partially explains the heterogeneous survival outcomes; recent studies have shown that radiomics phenotyping using machine learning (ML) techniques can identify these genomic markers[43,47] and predict overall survival with better accuracy than with the use of nonimaging markers.[41]

Summary

Introduction

Radiomics and radiogenomics have been shown to potentially complement biopsy by capturing regional genetic heterogeneity and by noninvasively evaluating various driver genes and prognostic markers at diagnosis.[43,44,47,56,57,58] Recent studies have shown that radiomics models can preoperatively predict O6methylguanine-DNA methyltransferase (MGMT) methylation, epidermal growth factor (EGFR) amplification, and EGFR variant III status in GBM.[48,58,59,60] Zhang et al[46] demonstrated that ML algorithms generated from preoperative MR imaging and clinical features of 120 patients with grade III and IV gliomas predicted isocitrate dehydrogenase (IDH) 1/2 status with accuracies of 86% and 89% in the training and validation cohorts, respectively.

Results

Conclusion