Case series illustrating the synergistic use of hydrogel spacer and MR-guidance to increase the radiotherapeutic index for localized prostate cancer
Bibliographic record
Abstract
BACKGROUND: The risk of gastrointestinal (GI) toxicity may limit the use of curative-intent radical radiotherapy (RT) for prostate cancer (PCa) in circumstances where morbidity of treatment may exceed an acceptable threshold. Rectal spacers are used to expand the distance between the anterior rectal wall and the prostate, consequently sparing the rectum from the high-dose region. CASE PRESENTATIONS: We report three clinical scenarios of PCa patients treated at our institution, where risk of RT-associated rectal toxicity may be increased: inflammatory bowel disease (IBD), salvage brachytherapy (BT) after previous external beam RT (EBRT), and tailored dose-escalation with focal BT to the gross tumor volume followed by stereotactic body RT. Prior to RT, a polyethylene glycol (PEG) hydrogel spacer was successfully placed in all cases. Treatment comprised magnetic resonance (MR) guided high dose-rate BT ± EBRT. All patients completed treatment uneventfully, without any significant GI toxicity at last follow-up. CONCLUSIONS: These cases illustrate the utility of PEG hydrogel spacer, where concerns of radiation induced toxicity may have previously limited the application of radiotherapy. The synergistic use of these novel devices together with MR-guided BT may expand the indications and therapeutic index of curative-intent RT-based treatments, while minimizing the risks of GI toxicity.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".