Utilizing clinical, pathological and radiological information to guide postoperative radiotherapy in prostate cancer
Bibliographic record
Abstract
INTRODUCTION: A detectable and rising PSA following radical prostatectomy is indicative of recurrent prostate cancer. Salvage radiotherapy (SRT) with/without androgen deprivation therapy represents the main treatment option for these patients and has been historically associated with a biochemical control rate of ~70%. To determine the optimal timing, diagnostic workup, radiotherapy dosefractionation, treatment volume, and use of systemic therapy, several informative studies have been conducted in the last decade. AREAS COVERED: This review examines the recent evidence to guide radiotherapy decision making in the SRT setting. Key topics include adjuvant vs salvage RT, utilization of molecular imaging and genomic classifiers, length of androgen deprivation therapy, inclusion of elective pelvic volume, and emerging role for hypofractionation. EXPERT OPINION: Recently reported trials, conducted in an era prior to the routine use of molecular imaging and genomic classifiers, have been pivotal in establishing the current standard of care for SRT in prostate cancer. However, decisions about radiation treatment and systemic therapy may be tailored based on available prognostic and predictive biomarkers. Data from contemporary clinical trials are awaited to define and establish individualized, biomarker-driven approaches for SRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".