Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments

Radiogenomics integrates imaging and genomic data to further refine precision oncology and is of particular interest in oncogene-driven lung cancer. By linking radiologic features with molecular alterations, radiogenomics aims to enable non-invasive tumor characterization, improve diagnostics, treatment planning, and disease monitoring. In oncogene-driven lung cancer, next-generation sequencing (NGS) has uncovered actionable oncogenes such as EGFR, KRAS, ALK, BRAF, MET, HER2, and fusions in ROS1, and NTRK, which have revolutionized the management and outcomes of patients with these alterations. Radiogenomics has the potential to overcome several challenges in the clinic, such as repeat tissue biopsies, which are invasive and may be inadequate due to inherent tumor heterogeneity, by leveraging imaging biomarkers from CT, PET, and MRI to infer genomic profiles. In this review, we discuss the many recent advances in the burgeoning field of radiogenomics. We discuss how specific radiological features in oncogene-driven NSCLC, are starting to aid in mutation prediction and personalized treatment selection, and explore how radiogenomics may enhance treatment response prediction and refine prognostic models beyond traditional staging. Finally, we explore some of the challenges in its clinical implementation, including standardization of imaging protocols, data harmonization, and some of the ethical considerations regarding patient privacy and finally, we evaluate current evidence beyond lung cancer.

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Publication Details

Journal
Discover Oncology
Published
2025-08-20
DOI
https://doi.org/10.1007/s12672-025-03321-5
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
Controls
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article

Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments

John Kavanagh, Niamh Coleman, James Ryan
2 citations
Discover Oncology
Radiomics and Machine Learning in Medical Imaging
2.72
article

Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments

John Kavanagh, Niamh Coleman, James Ryan
article en
2 citations

Abstract

Radiogenomics integrates imaging and genomic data to further refine precision oncology and is of particular interest in oncogene-driven lung cancer. By linking radiologic features with molecular alterations, radiogenomics aims to enable non-invasive tumor characterization, improve diagnostics, treatment planning, and disease monitoring. In oncogene-driven lung cancer, next-generation sequencing (NGS) has uncovered actionable oncogenes such as EGFR, KRAS, ALK, BRAF, MET, HER2, and fusions in ROS1, and NTRK, which have revolutionized the management and outcomes of patients with these alterations. Radiogenomics has the potential to overcome several challenges in the clinic, such as repeat tissue biopsies, which are invasive and may be inadequate due to inherent tumor heterogeneity, by leveraging imaging biomarkers from CT, PET, and MRI to infer genomic profiles. In this review, we discuss the many recent advances in the burgeoning field of radiogenomics. We discuss how specific radiological features in oncogene-driven NSCLC, are starting to aid in mutation prediction and personalized treatment selection, and explore how radiogenomics may enhance treatment response prediction and refine prognostic models beyond traditional staging. Finally, we explore some of the challenges in its clinical implementation, including standardization of imaging protocols, data harmonization, and some of the ethical considerations regarding patient privacy and finally, we evaluate current evidence beyond lung cancer.

Discover OncologyVol. 16(1)
St. James's Hospital (IE)
Good health and well-being
Openalex Percentile: Top 9%
Radiomics and Machine Learning in Medical Imaging
2.72
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