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.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

“…AI has the potential to speed up the creation of life-saving medications, thereby saving billions of dollars that could be invested in maintaining healthy ecosystems [ 13 ]. A start-up supported by the University of Toronto recently created a supercomputer-based algorithm that resembles and evaluates millions of potential medications that help in forecasting their effectiveness against the Ebola virus.…”

“…It places a significant emphasis on the AI system’s resilience while only a slight emphasis is placed on its authenticity [ 170 ]. It introduces bias into the AI system [ 13 , 16 , 155 , 168 , 169 , 171 ]. It is also important to note that the database contains specific regional patient characteristics; as a result, the model may under or overestimate diagnosis and treatment findings for different ethnicities or comorbidities [ 164 ].…”

“…The Internet of Medical Technology (IoMT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) are currently the primary drivers [ 8 , 9 ]. Innovation is becoming the centerpiece [ 10 , 11 , 12 , 13 ]. AI technological development improves existing systems, especially medical imaging [ 9 , 14 ] and coronary artery disease diagnosis [ 15 , 16 ], reducing human error, increasing patient care overall, and making doctors’ responsibilities easier [ 17 ].…”

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

“…CXR scans are commonly used in clinical practice since they are inexpensive, simple to perform, give a quick scan for the patient as two-dimensional (2D) images, and can be widely used for diagnosis and treatment of lung and cardiovascular diseases [ 34 , 35 ]. Although X-rays are frequently used, they have side effects such as exposure to ionizing radiation harmful to the human body and relatively low information when compared to other imaging methods; Computerized Tomography (CT): is a more advanced imaging test that can be used to detect disorders such as cancer that an X-ray could miss [ 36 , 37 , 38 , 39 ]. A CT scan is a series of X-rays taken from various angles that are patched together to create a complete image.…”

“…Computerized Tomography (CT): is a more advanced imaging test that can be used to detect disorders such as cancer that an X-ray could miss [ 36 , 37 , 38 , 39 ]. A CT scan is a series of X-rays taken from various angles that are patched together to create a complete image.…”