From Radiomics to Radiogenomics: Decoding Renal Cell Carcinoma Biology for Precision Medicine-a narrative review

Renal cell carcinoma is a prevalent malignancy affecting the urinary system and poses significant challenges in precision diagnosis and treatment. Although medical imaging technologies have been widely applied in renal cell carcinoma screening, traditional imaging diagnostics have limitations due to their high degree of subjectivity, relying primarily on the doctor's experiential judgment. The advent of radiomics presents a groundbreaking method for tackling this issue-by extracting high-throughput, deep-level information from conventional medical images to achieve a quantitative assessment of tumor characteristics. Furthermore, the fusion of radiomics and genomics has led to radiogenomics, which combines imaging features with molecular data, enabling the non-invasive evaluation of tumor biological behavior, molecular heterogeneity, and microenvironmental features, thereby providing a more detailed, accurate, and personalized assessment. In this review, we summarize the role radiomics and radiogenomics play in the diagnosis, prediction, and adjuvant treatment of renal cell carcinoma. Radiomics has demonstrated potential in classifying renal cell carcinoma subtypes, predicting patient prognosis, and forecasting disease progression. Radiogenomics further links imaging features to gene mutations and the tumor microenvironment, enabling non-invasive assessment of renal cell carcinoma biology and providing new approaches to diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: By reviewing existing research, we summarize how radiomics and radiogenomics address key clinical challenges in the diagnosis and treatment of renal cell carcinoma, providing non-invasive solutions to overcome tumor heterogeneity and guide precision oncology. KEY POINTS: Renal cell carcinoma lacks reliable non-invasive biomarkers for precision diagnosis and characterization. Radiogenomics bridges imaging and molecular biology for precise predictions. Radiogenomics lacks full multi-omics integration despite data growth.