Findings on Diagnostic Magnetic Resonance Imaging Before Radiotherapy for Prostate Cancer
Bibliographic record
Abstract
Purpose Pre-treatment diagnostic magnetic resonance imaging (MRI) is used in prostate cancer detection and staging; however, little is known about its potential for radiotherapy treatment decision, or its prognostic value. We investigated the findings on pre-treatment MRI and its potential influence on treatment decisions, and its ability to predict biochemical recurrence in patients treated with radiotherapy. Methods Files of patients treated by radiotherapy from 2014 to 2022 were searched for if they had had an MRI within 12 months before radiotherapy. Prostate Imaging Reporting & Data System (PI-RADS) score, index lesion diameter and the presence of organ confined disease or extra-prostatic extension were correlated with their Cancer of the Prostate Risk Assessment (CAPRA) score. Distribution of radiological and clinical features between groups were estimated using a chi-squared test. Results Out of 1280 patients, 314 (24.5%) had an MRI. The distribution depended on the treatment received: 22.5% who received low-dose rate (LDR) brachytherapy as monotherapy, 24.0% treated with high-dose rate (HDR) boost and 32.0% treated with external-beam radiotherapy (EBRT) ( P = .017). The CAPRA score significantly correlated with the PI-RADS score (r = .342, P < .01) and the diameter of the index lesion (r = .473, P < .01). A clinically significant number of 22% patients with CAPRA ≤ 3 disease presented with lesions ≥15 mm and were less likely to be treated with LDR monotherapy ( P < .01). 39 patients had a recurrence, only 5 had an MRI: 4 had a lesion of ≥20 mm and 3 a seminal vesicle invasion. Conclusion More than twenty percent of patients with CAPRA ≤3 presented on MRI a ≥15 mm lesion. An MRI could potentially affect treatment choice, and although exploratory our results suggest an important prognostic potential.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".