Predictors of prostate bed recurrence on magnetic resonance imaging in patients with rising prostate-specific antigen after radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Radical prostatectomy (RP) is a standard treatment modality for localized prostate cancer. Biochemical failure after RP is usually evaluated with whole-body imaging to exclude distant metastatic disease, and pelvic magnetic resonance imaging (MRI) to detect local recurrence in the prostatectomy bed. The goal of this study is to correlate disease characteristics and demographic data in patients with rising prostate-specific antigen (PSA) after RP to determine association with MRI-detected cancer recurrence. METHODS: Sixty-four patients who underwent pelvic MRI for rising PSA after RP and had complete clinical and pathological data available were included. Using Chi-squared testing, we analyzed PSA levels, pathological disease characteristics (prostate cancer risk group, Gleason score, extracapsular extension, positive surgical margin, seminal vesicle involvement, perineural invasion, lymphovascular invasion, and PSA level before MRI), time from surgery to biochemical failure, and patient demographic characteristics as potential predictors of MRI-detected local recurrence. RESULTS: Definite MRI-detected local recurrence was observed in 17/64 patients (27%). Eleven (17%) patients had a suspicious lesion with the differential of scarring, retained seminal vesicle, or recurrent cancer. Thirty-six (56%) patients had no evidence of tumor in the prostate bed or pelvis on MRI. Patient race was associated with likelihood of detecting a prostate nodule on MRI (p=0.04), with African American patients having 82% lower odds of MRI-detected tumor recurrence compared with white patients (p=0.045). No other tumor or patient characteristic was significantly associated with MRI-detected recurrence. CONCLUSIONS: African American patients with biochemical failure after RP are less likely to have MRI-detectable recurrence in the prostate bed compared with white patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".