Definitive Stereotactic Body Radiotherapy (SBRT) for Extracranial Oligometastases
Bibliographic record
Abstract
PURPOSE: Stereotactic body radiotherapy (SBRT) is often used to treat patients with oligometastases (OM). Yet, patterns of SBRT practice for OM are unknown. Therefore, we surveyed radiation oncologists internationally, to understand how and when SBRT is used for OM. METHODS: A 25-question survey was distributed to radiation oncologists. Respondents using SBRT for OM were asked how long they have been treating OM, number of patients treated, organs treated, primary reason for use, doses used, and future intentions. Respondents not using SBRT for OM were asked reasons why SBRT was not used and intentions for future adoption. Data were analyzed anonymously. RESULTS: We received 1007 surveys from 43 countries. Eighty-three percent began using SBRT after 2005 and greater than one third after 2010. Eighty-four percent cited perceived treatment response/durability as the primary reason for using SBRT in OM patients. Commonly treated organs were lung (90%), liver (75%), and spine (70%). SBRT dose/fractionation schemes varied widely. Most would offer a second course to new OM. Nearly all (99%) planned to continue and 66% planned to increase SBRT for OM. Of those not using SBRT, 59% plan to start soon. The most common reason for not using SBRT was lack of clinical efficacy (48%) or lack of necessary image guidance equipment (34%). CONCLUSIONS: Radiation oncologists are increasingly using SBRT for OM. The main reason for not using SBRT for OM is a perceived lack of evidence demonstrating clinical advantages. These data strengthen the need for robust prospective clinical trials (ongoing and in development) to demonstrate clinical efficacy given the widespread adoption of SBRT for OM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".