Stereotactic Body Radiation Therapy for Gynecologic Malignancies: A Case-Based Radiosurgery Society Practice Review
Bibliographic record
Abstract
PURPOSE: The use of stereotactic body radiation therapy (SBRT) for gynecologic malignancies is controversial. We discuss certain circumstances when highly precise SBRT may be a useful tool to consider in the management of selected patients. METHODS AND MATERIALS: Case selection included the following scenarios, the first 2 with palliative intent, para-aortic nodal oligorecurrence of ovarian cancer, pelvic sidewall oligorecurrence of cervical cancer, and inoperable endometrial cancer boost after intensity modulated radiation to the pelvis treated with curative intent. Patient characteristics, fractionation, prescription dose, treatment technique, and dose constraints were discussed. Relevant literature to these cases was summarized to provide a framework for treatment of similar patients. RESULTS: Treatment of gynecologic malignancies with SBRT requires many considerations, including treatment intent, optimal patient selection, fractionation selection, tumor localization, and plan optimization. Although other treatment paradigms including conventionally fractionated radiation therapy and brachytherapy remain the standard-of-care for definitive treatment of gynecologic malignancies, SBRT may have a role in palliative cases or those where high doses are not required due to the unacceptable toxicity that may occur with SBRT. CONCLUSIONS: A case-based practice review was developed by the Radiosurgery Society to provide a practical guide to the common scenarios noted above affecting patients with gynecologic malignancies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".