Prostate cancer hypoxia as a predictor of early biochemical and local failure after radiotherapy.
Bibliographic record
Abstract
20 Background: Tumor hypoxia is an important determinant of patient outcome in many human malignancies, and has been associated with radioresistance and the development of metastases. The aim of this study was to determine the predictive effect of hypoxia in prostate cancer patients treated with RT. Methods: A total of 256 patients with clinically localized prostate cancer underwent pre-treatment, transrectal, ultrasound-guided measurement of tumor oxygen using a needle electrode. The median pO2 (mpO2) was 6.7 mm Hg and the median hypoxic proportion <10 mm Hg (HP10) was 0.63. Most patients were treated with IMRT to the prostate alone using doses in the range of 75.6-79.8 Gy. Sixty-one received neoadjuvant and concurrent hormonal therapy. The Phoenix definition of biochemical relapse was used, and the median follow-up was 5.4 years. Results: The 5-year bRFR was 78%. High PSA and Gleason score were independently associated with biochemical relapse, and formed the baseline clinical multivariate model. The effect of hypoxia was found to vary with the duration of patient follow- up. HP10, when added to the clinical multivariate model as a time-dependent variable, was a significant, independent predictor of early bRFR (p=0.015). The predictive effect of hypoxia diminished with increasing follow-up and was lost by 36 to 48 months. The relationship between hypoxia and early biochemical recurrence was more pronounced when the analysis was restricted to 144 patients with bulk tumor at the site of the oxygen measurements (p=0.008). Prostate biopsy was performed in 73 patients a median of 36 months after completing RT. Hypoxia was the only factor predictive of local recurrence in this sub-group, with the effect being greatest early in follow-up (p=0.038). Conclusions: This is the largest clinical study of prostate cancer hypoxia with direct measurement of tumor oxygen levels. It suggests that hypoxia in the index tumor increases the risk of recurrence early after completing RT but not at longer times. The results imply a complex interaction between hypoxia and local vs. distant failure, which may be better elucidated with longer follow-up. No significant financial relationships to disclose.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".