Abstract
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.
Highlights
Advances in genomics and the far-reaching effects of precision medicine have synergistically accelerated research by integrating the individual characteristics of patients [1]
The results demonstrated that the interactive method produced more robust features than the semi-automatic method; the robustness of the radiomic features varied by categories
Many methods have been used successfully to evaluate the performance of radiomics models; the receiver-operating characteristic (ROC) curve is the method most commonly utilized for discrimination analysis and the concordance index is usually used for validation of survival analysis [52]
Summary
Advances in genomics and the far-reaching effects of precision medicine have synergistically accelerated research by integrating the individual characteristics of patients [1]. Mutations of the Kirsten rat sarcoma viral oncogene (KRAS), epidermal growth factor receptor (EGFR), and anaplastic lymphoma kinase (ALK) genes have been identified to be common oncogenic drivers [5] These abnormalities of specific molecular and signaling pathways can be used as genomic biomarkers that provide personalized information about diagnosis, treatment, and prognosis, and contribute to selection of the optimal therapeutic strategy. The spatial and temporal variables of gene expression may cause changes in various biological processes in the tumor, including apoptosis, cellular proliferation, growth patterns, and angiogenesis These alterations occur at the molecular and cellular levels and, to a large extent, are shown as heterogeneous imaging features, which can be transformed into varying degrees. We provide a brief overview of a feasible imaging protocol for radiogenomics
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