Re-Irradiation for Recurrent Cervical Cancer: A State-of-the-Art Review
Bibliographic record
Abstract
The recurrence rate of cervical cancer after primary treatment can reach 60%, and a poor prognosis is reported in most cases. Treatment options for the recurrence of cervical cancer mainly depend on the prior treatment regimen and the location of recurrent lesions. Re-irradiation is still considered as a clinical challenge, owing to a high incidence of toxicity, especially in in-field recurrence within a short period of time. Recent advances in radiotherapy have preliminarily revealed encouraging outcomes of re-irradiation. Several centers have concentrasted on stereotactic body radiation therapy (SBRT) for the treatment of well-selected cases. Meanwhile, as the image-guiding techniques become more precise, a better dose profile can also be achieved in brachytherapy, including high-dose-rate interstitial brachytherapy (HDR-ISBT) and permanent radioactive seed implantation (PRSI). These treatment modalities have shown promising efficacy with a tolerable toxicity, providing further treatment options for recurrent cervical cancer. However, it is highly unlikely to draw a definite conclusion from all of those studies due to the large heterogeneity among them and the lack of large-scale prospective studies. This study mainly reviews and summarizes the progress of re-irradiation for recurrent cervical cancer in recent years, in order to provide potential treatment regimens for the management of re-irradiation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.005 | 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".