Radiotherapy remains a cornerstone of cancer management, yet conventional dose prescription relies largely on anatomical imaging and population-based fractionation paradigms. Interpatient variability in tumor radiosensitivity and normal tissue toxicity underscores the need for biologically informed treatment personalization. Radiogenomics, the integration of quantitative imaging features with tumor genomic alterations to predict radiation response and toxicity, and radiotranscriptomics, which incorporates gene expression profiles to capture dynamic biological responses to ionizing radiation, have emerged as complementary approaches for predicting radiation response and guiding dose and fractionation. Early radiogenomic studies demonstrated associations between quantitative imaging features and underlying molecular pathways, laying the foundation for noninvasive biomarkers of radiosensitivity. More recently, transcriptomic signatures and genome-based models such as the genomic-adjusted radiation dose have shown promise in predicting clinical outcomes and toxicity across multiple tumor sites. Advances in artificial intelligence and multi-omics integration further enable scalable, data-driven precision radiotherapy frameworks. This review synthesizes current evidence on radiogenomics and radiotranscriptomics, focusing on their biological rationale, clinical validation, and translational potential for tailoring radiotherapy dose and fractionation in modern oncology practice. 1-4
Bisht et al. (Thu,) studied this question.