Characteristics of Oligo-Recurrence and Treatment Selection in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Recent advances in technology and pharmacologic agents have significantly improved both local and systemic therapies, making the treatment of non-small cell lung cancer (NSCLC) more effective and less invasive. However, recurrence after radical resection remains a major clinical challenge. Among the various recurrence patterns, oligo-recurrence-particularly metachronous oligo-recurrence, characterized by a limited number of metastatic lesions appearing after a disease-free interval-has gained attention due to its potential for long-term survival and even cure through local therapy. Concurrently, systemic treatments have advanced with the development of molecularly targeted therapies and immune checkpoint inhibitors. Numerous studies have demonstrated their clinical efficacy, resulting in significant improvements in patient prognosis. Therefore, selecting an appropriate treatment strategy for recurrent NSCLC involves a broad spectrum of therapeutic options, including targeted therapies, immune checkpoint inhibitors, and conventional chemotherapy. Treatment decisions are particularly complex in cases of oligo-recurrence, where local therapy is feasible, making it challenging to choose the best approach from the available options. This narrative review summarizes current evidence from retrospective and ongoing prospective trials and discusses the clinical characteristics and treatment strategies for oligo-recurrent NSCLC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".