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

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

“…Radiogenomics is a promising approach to realizing precision medicine by using non-invasive imaging technology to monitor the molecular behavior of the tumor, as the latest studies reported (56)(57)(58)(59)(60). For instance, the tumor mutational burden risk can be predicted in both primary and livermetastatic colorectal cancer (AUCs: 0.732 and 0.812) by using radiogenomics analysis based on computed tomography (CT) images (57).…”

“…Compared with previous radiogenomics studies, although our work overcame a few shortcomings, it still had some limitations (56,66,67). First, we used two radiogenomics cohorts of BC: one was a local single-center dataset for discovery (n = 174) and another was a public multi-center dataset for validation (n = 72).…”

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

“…Furthermore, one could study the brain connectivity in brain tumor patients [ 160 , 161 ]. Association studies can be incorporated, which study the relationships between brain tumor radiomics and genomics for survival analysis [ 129 , 162 ]. Radiogenomics is inevitable and needs further high-level, multi-center, multi-intuitional, multi-geographical research.…”

“…Hence, detecting the status of various genomes in genetic mutation in the brain tumor can further help the diagnosis and prognosis process in a personalized treatment plan. Using ML and DL technologies, AI can detect the genetic status from radiography [ 129 , 130 ].…”

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

“…Radiogenomics, the combination of “ Radiomics ” and “ Genomics ”, refers to the use of imaging features or surrogates to determine genomic signatures and advanced biomarkers in tumours. These biomarkers can then be used for clinical management decisions, including prognostic, diagnostic and predictive precision of tumour subtypes [ 131 ]. The workflow of a radiogenomics study can be commonly classified into five different stages ( Figure 5 ): (1) image acquisition and pre-processing, (2) feature extraction and selection from both the medical imaging and genotype, (3) association of radiomics and genomics features, (4) data analysis using machine learning models and (5) final radiogenomics outcome model [ 132 ].…”