Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group
Bibliographic record
Abstract
Background: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for several primary and metastatic malignancies; however, considerable heterogeneity remains in its definition, clinical indications, and technical delivery. Methods: In May 2025, the SBRT Focus Group of the European Society for Radiotherapy and Oncology (ESTRO), in collaboration with International Stereotactic Radiosurgery Society (ISRS), the Radiosurgery Society (RSS), and the Japanese Society for Radiation Oncology (JASTRO), conducted a global survey. A 44-item questionnaire explored SBRT indications, technical aspects, dose/fractionation, and barriers to implementation. Descriptive statistics summarized the responses. Results: Overall, 289 professionals from 59 countries participated. Routine use of SBRT was reported by 96.6 % of respondents, with lung, bone, liver and prostate as the most frequent indications. Pancreatic tumor (48.4 %), renal cell carcinoma (46.4 %), and ventricular tachycardia (12.4 %) represented emerging indications. C-arm linacs (89.2 %) and in-room Cone beam CT (CBCT) (92.0 %) were the dominant technologies. Motion management relied mainly on 4D-CT internal target volume (ITV) (88.9 %) and deep inspiration breath-hold (DIBH) (57.8 %). Fractionation was consistent for lung and prostate but heterogeneous for liver, and pancreas. Only 3.5 % reported routine use of online adaptive SBRT, while 61.5 % reported artificial intelligence (AI) use, mainly for organs-at-risk delineation. Key barriers included limited clinical trial funding (35.2 %), high equipment costs (34.2 %), insufficient reimbursement (27.7 %), and workforce shortages (33.9 %). Conclusions: This ESTRO international survey provides the first global overview of SBRT practices. It demonstrates broad adoption but also substantial variability, highlighting the need for consensus guidelines, greater trial access, and expanded education to harmonize SBRT delivery and ensure equitable care worldwide.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".