Adrenal Oligometastases Secondary to Non-small Cell Lung Cancer—What is the Optimal Treatment Approach?
Bibliographic record
Abstract
Background: Five-year overall survival (OS) for patients with stage IV non-small cell lung cancer (NSCLC) is a dismal 1%. However, approximately 7% have limited or solitary metastases, including to the adrenal gland. Radical treatment of these oligometastases (OM) could increase local control and improve OS. Our objective was to critically analyze data describing aggressive treatment of adrenal OM secondary to NSCLC. Methods: A literature search examining English publications describing surgery or radiotherapy (RT) was performed, supplemented by searching reference lists. Case reports of three or fewer patients, and articles from which NSCLC- or adrenal-specific clinical outcomes could not be abstracted, were excluded. Results: Twenty-nine studies met eligibility criteria (521 patients), 26 retrospective. No publications directly compare modalities. Four surgery studies described contemporaneous patients treated with palliative chemotherapy (CH) alone. Reported median OS ranged from 9.5–64 months after adrenalectomy, 8–23 months after RT, and 6–8.5 months after CH. Local failure after surgery was 14%, with response rates after RT 57–75%. Both appear well-tolerated. Conclusions: In patients with an adrenal OM secondary to NSCLC, aggressive treatment should be considered. However, due to the paucity of high quality evidence, it is unclear at present whether this approach alters the natural history of the disease.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".