Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".