Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Abstract

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Keywords: brain tumor; brain tumor characterization; classification; genomics; radiogenomics; radiomics; risk-of-bias; segmentation.

Figure A1

(a) MRI scanner machine. (b) Working procedure of MRI.

Figure A2

The three prominent views of MRI scanning: axial, sagittal, coronal (Top), and brain MRI slices of T1-weighted vs. T2-weighted and flair (Bottom).

Figure A3

Comparison of standard MRI with (a) Gad-enhanced, (c) functional, (b) diffusion, and (d) perfusion MRI.

Figure A4

CT scans of brain tumors (glioma). (Image source: www.wellcomecollection.org, accessed on 9 December 2021).

Figure A5

¹⁸F-PET and ¹⁸F-FDG PET scans. (Image source: www.semanticscholar.org, accessed on 9 December 2021).

Figure A6

^99mTc-HMPAO SPECT scans [172].

Figure 1

The PRISMA framework for the flow diagram of the selection process.

Figure 2

Statistical distribution for radiogenomics studies: (a) country-wise; (b) types of AI technology; (c) types of AI models; and (d) AI-based classifiers used in radiogenomics. Notes: ML: machine learning, DL: deep learning, TL: transfer learning, CNN: convolutional neural network, DNN: deep neural network, VGG: visual geometric group, SVM: support vector machine, ANN: artificial neural network, K-NN: K-nearest neighbor, DT: decision tree.

Figure 3

Statistical distribution for radiogenomics studies: (a) imaging modality; (b) anatomical area; (c) performance evaluation. Notes: CT: computer tomography, PET: positron emission tomography, MRI: magnetic resonance imaging, AUC: area under curve, SD: standard deviation.

Figure 4

The distribution of increasing dataset size in various radiogenomics studies [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71].

Figure 5

Glia cells of the neuron and their environment of the central nervous system and peripheral nervous system [88].

Figure 6

Chemical reaction and metabolic pathways present in a brain tumor cell, with emphasis on enzymatic effectors—IDH1 and IDH2 mutations [92].

Figure 7

Radiomics workflow for brain lesion characterization. Notes: T1: T1-weighted MRI, T2: T2-weighted MRI, T1-CE: T1-contrast-enhanced, FLAIR: fluid-attenuated inversion recovery.

Figure 8

The workflow of radiogenomics for brain tumor genomics and disease characterization. Notes: IDH: isocitrate dehydrogenase, TP53: tumor protein53, MGMT: O6-methylguanine DNA methyltransferase, EGFR: epidermal growth factor receptor, PTEN: phosphatase and tensin homolog, HER2: human epidermal growth factor receptor 2.

Figure 9

The ranking score technique shows the frequency distribution of radiogenomics studies in descending order succeeded by the cumulative plot, showing the raw cut-off mark for bias analysis [51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71].