Radiogenomics in oncology: image-genome integration for personalized medicine
DOI:
https://doi.org/10.56294/saludcyt20252144Keywords:
Radiogenomics, image interpretation, magnetic resonance imaging, precision medicine, biomarkers, tumor, artificial intelligence, oncologyAbstract
Introduction: radiogenomics, which combines medical imaging data with genomic profiling, has emerged as a key tool in precision oncology. This noninvasive approach improves the diagnosis and prognosis of tumors such as lung, rectal, glioma, and breast cancer.
Objectives: A systematic review (PRISMA 2020) was conducted of studies published between 2020 and 2025, extracted from PubMed, Scopus, Web of Science, ScienceDirect, and the Cochrane Library. Of 670 articles found, 21 met the inclusion criteria.
Methods: this was a systematic review following the PRISMA 2020 guidelines. Original studies, reviews, and meta-analyses published in English or Spanish were included. Searches were conducted in PubMed, Scopus, Web of Science, ScienceDirect, and the Cochrane Library.
Results: of a total of 670 articles retrieved, 21 met the inclusion criteria. Most studies demonstrated a high predictive capacity of radiogenomic models to identify mutations such as EGFR and KRAS.
Conclusions: this study underscores the need to establish multicenter protocols and robust validations to ensure their clinical applicability and consolidate their role in personalized medicine.
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Copyright (c) 2025 Cinthia Katherine Galarza Galarza, Lorena Bibiana Erazo Beltrán (Author)
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