New Strategies in Stereotactic Radiotherapy for Oligometastases
Bibliographic record
Abstract
Patients with metastatic solid tumors are usually treated with palliative intent. Systemic therapy and palliative radiation are often used, with the goals of prolonging survival or maintaining quality of life, but not of cure. In contrast to this paradigm, the theory of oligometastasis suggests that some patients who have a small number of metastases may be amenable to cure if all lesions can be eradicated. Aggressive treatment of patients with oligometastases, using either surgery or radiotherapy, has become more common in the past decade, yet in most situations, no randomized evidence is available to support such an approach. Stereotactic ablative radiotherapy (SABR) is a novel treatment for oligometastases, delivering large doses of radiotherapy in only a few treatments, with excellent rates of local control, and appears to be an excellent noninvasive alternative to surgical resection of metastases. This article reviews recent biologic and clinical data that support the existence of the oligometastatic state and discusses gaps in this evidence base. The emerging role for SABR in the management of this challenging patient population is discussed with a focus on ongoing clinical trials in an attempt to improve overall survival, delay progression, or induce immunologic anticancer effects through the abscopal effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".