Oligometastatic NSCLC: Current Perspectives and Future Challenges
Bibliographic record
Abstract
Oligometastatic non-small cell lung cancer (NSCLC) represents a separate entity with a different biology and prognosis compared to stage IV NSCLC. Challenges range from the very definition of oligometastatic disease to the timing and techniques of local treatments, and their benefit in prolonging patient survival. Most of the international consensus and guidelines agree on the need for shared criteria, such as appropriate stadiation and even tissue biopsy if needed, in order to select patients that could really benefit from personalised strategies. Multidisciplinary evaluation is crucial in order to define if every lesion is amenable to radical local treatment, which appears to be the most important criterion across different guidelines. A distinction must be made depending on the time of oligo-disease detection, separating de novo oligometastatic disease from oligorecurrence, oligoprogression and oligoresidual disease. These separate entities imply a different biology and prognosis, and treatment strategies consequently must be tailored. Locoregional approaches are therefore often contemplated in order to ensure the best outcome for the patient. In non-oncogene-addicted disease, the advent of immune checkpoint blockers (ICBs) allows physicians to take into consideration consolidative treatments, but timing, technique and subsequent systemic treatment remain open issues. In oncogene-addicted NSCLC, local treatments are nowadays preferably reserved to cases of oligoprogression, but the advent of new, more potent drugs might challenge that. In this review, we summarised the current knowledge, consensuses and data from retrospective and prospective trials, with the aim of shedding some light on the topic and emphasising the unmet clinical need.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".