The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

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.

Keywords: artificial intelligence; deep learning; precision medicine; radiogenomics; radiological imaging.

Figure 1

The general radiomics study workflow. Step 1: image acquisition. Step 2: region of interest identification and segmentation. Step 3: quantitative image feature extraction. Step 4: data mining and informatics analysis. The figure was reprinted by ref (14) with permission from the publisher.

Figure 2

The classifications and corresponding examples of quantitative radiomics features. The figure was reproduced according to ref (14). with permission from the publisher.