Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Abstract

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Keywords: artificial intelligence; oncology; precision medicine; radiogenomics; radiomics.

FIGURE 1

Schematic diagram illustrates the comprehensive integration of radiomics with omics data for precise cancer care. The first step involves collecting data resources, including imaging and biological samples. From these resources, various dimensions of radiomic features and molecular signatures of cancers are extracted and refined. Ultimately, radiomics and omics data are interconnected and integrated using advanced artificial intelligence algorithms to construct accurate clinical prediction models.

FIGURE 2

Potential enhanced clinical workflow with radiogenomics interventions. Radiogenomics offers the potential to noninvasively predict key molecular characteristics, including their temporal and spatial heterogeneity, at the initial diagnosis and posttreatment monitoring stages of cancer. This can help in discovering therapeutic targets, enhancing cancer prognosis, and predicting treatment response. Ultimately, radiogenomics can guide precision diagnosis and treatment of cancers, enhancing patient outcomes.