Prognostic factors of biochemical recurrence and impact of pre-treatment MRI for prostate cancer radiotherapy
Bibliographic record
Abstract
OBJECTIVES: We conducted an analysis of the prognostic implications of MRI findings prior to radiotherapy in patients diagnosed with prostate cancer. METHODS: Patients from our institutional database who were treated with radiotherapy between 2014-2024 were searched for diagnostic MRI. The prognostic significance of PI-RADS score, index-lesion diameter, and Cancer of the Prostate Risk Assessment (CAPRA) score on biochemical recurrence was analysed. RESULTS: Of the 1480 patients, 499 (33.7%) underwent a diagnostic pre-treatment MRI; 49.5% were treated with low-dose brachytherapy, 29.8% with external beam radiation therapy (EBRT) plus a high-dose rate brachytherapy boost, and 20.7% with EBRT alone. Among the patients who underwent MRI, 404 (81%) had PI-RADS 4-5, including 35% with lesions ≥15 mm and 20% with lesions ≥20 mm. The median follow-up period was 44 months (IQR:23-66). Among the 78 patients who subsequently experienced biochemical recurrence, 16 underwent a diagnostic MRI prior to treatment. CAPRA score did not correlate with lesion diameter (P = 0.4). In univariate analysis, lesions ≥15 mm (P = 0.026) and ≥20 mm (P < 0.001) were significant predictors, as was CAPRA score (P < 0.001). In multivariate analyses, lesion size ≥20 mm (hazard ratio [HR], 3.49; 95%CI:1.25-9.76, P = 0.017) but not ≥15 mm significantly predicted recurrence. Stratified by CAPRA, only in high-risk cancers (score 6-10, 21% of patients) was a lesion ≥20 mm a significant predictor (P < 0.001). CONCLUSIONS: We determined that a lesion on MRI with a diameter of ≥20 mm was an independent prognostic factor for biochemical recurrence, particularly in high-risk cancers. Whether the radiation dose-escalation of these lesions can improve clinical outcomes must be determined. ADVANCES IN KNOWLEDGE: We found that a prostate lesion on MRI with a diameter ≥20 mm was associated with poorer outcomes following radiotherapy.
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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 | 0.004 |
| 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".