Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it