Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Abstract

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

“…ROI is, however, a crucial part because of the unclear margin, shape, size, and location of the tumor. The preprocessing, data handling, and segmentation of radiomics data provide better accuracy on the AI model for better diagnosis and prognosis of cancer [ 8 ].…”

“…Advancements of different estimation techniques, such as genomic sequencing and medical radiography of cancer, have enormously augmented the quantity of patient data accessible to the clinician perspective to radiogenomics. Indeed, AI, the advanced set of computational algorithms, is perfect for this and can easily deal in radiology from image acquisition, image reconstruction, feature extraction and selection, data analysis, and developing models to analyze cancer, treatment prognosis, follow-up planning, and many other aspects [ 8 ]. Figure 10 represents how AI improved the entire radiological workflow in current clinical practice.…”

“…Apart from classification or regression techniques in radiogenomics, AI could be used in several other applications such as cancer heterogeneity analysis, progression of tumors, recurrence, etc. [ 8 ]. AI improves the entire radiological workflow in three key ways: productivity, quantity, and precision.…”

“…However, due to the technical complexity, cost, and turnaround time, a vast scale genome-based characterization of cancer is not yet routinely adapted for all types of cancers [ 5 , 6 , 7 ]. In existing clinical practices, due to the heterogeneous behavior of cancer, molecular profiling is often limited, and heterogeneity of the tumor is repeatedly missed when a portion of the cancer is examined [ 8 ]. Throughout the treatment, determination of molecular targets requires ex vivo postoperative analysis of the resected tumor or biopsy sample.…”

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

“…OS, recurrence, genomics, etc. 18 , 24 , 25 . In this study, we aimed to assess the potential of integrating multi-omics prognostic characteristics, including clinical measures, radiomics, MGMT methylation, and genomics, to predict OS in GBM patients.…”