A Systematic Review of SBRT Boost for Cervical Cancer Patients Who Cannot Benefit from Brachytherapy
Bibliographic record
Abstract
Stereotactic body radiation therapy has emerged as a promising alternative to brachytherapy, delivering high doses to tumors with precision while sparing surrounding organs. This systematic review evaluates the role of SBRT as a boost for patients who are ineligible for brachytherapy. A total of 17 studies, involving 288 patients, were analyzed, focusing on dosimetric parameters and toxicity. The radiation regimens varied in dose and fractionation schedules, with external beam doses ranging from 44 to 61.6 Gy, and SBRT boost doses ranging from 5 to 30 Gy. The total EQD2 doses were between 50.5 and 92.4 Gy. The results indicate adequate tumor control with SBRT, with local control rates ranging from 57% to 95.5%. The acute genitourinary and gastrointestinal toxicities were mostly grade 1 or 2, while late toxicities were less common. The overall survival rates varied between 34% and 96%. These results suggest that SBRT boost offers a viable option for cervical cancer patients ineligible for brachytherapy, with acceptable toxicity and promising survival outcomes. Nevertheless, the scarcity of data, which mainly originate from small studies with patients having varied stages of disease, as well as the lack of long-term follow up with SBRT, should encourage clinicians to utilize brachytherapy whenever suitable as a boost in these patient cohorts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".