Image-Guided Brachytherapy for Rectal Cancer: Reviewing the Past Two Decades of Clinical Investigation
Bibliographic record
Abstract
(1) Background: The introduction of total mesorectal excision (TME) for rectal cancer has led to improvement in local recurrence (LR) outcomes. Furthermore, the addition of preoperative external beam radiotherapy to TME reduces LR to less than 6%. As a trade-off to these gradual improvements in local therapies, the oncology community's work is now focusing on mitigating treatment-related toxicities. In other words, if a small proportion of 4-6% of rectal cancer patients benefit from additional local therapy beyond TME, the burden of acute and long-term side effects must be considered with care. (2) Methods: With the introduction of better-quality imaging for tumor visualization and treatment planning, a new conformed radiation treatment was introduced with high-dose-rate endorectal brachytherapy. The treatment concept was tested in phase I and II studies: first in the pre-operative setting, and then as a boost after external beam radiation therapy, as a dose-escalation study, to achieve higher local tumor control. (3) Results: HDREBT is safe and effective in achieving a high tumor regression rate and was well tolerated in a phase II multicenter and two matched-pair studies. (4) Conclusions: HDREBT is a conformed radiation therapy that is safe and effective, and is presently explored in a phase III dose-escalation study in the NOM of patients with operable rectal cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".