Prostate volume estimations using magnetic resonance imaging and transrectal ultrasound compared to radical prostatectomy specimens
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the accuracy of prostate volume estimates in patients who received both a preoperative transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI) in relation to the referent pathological specimen post-radical prostatectomy. METHODS: Patients receiving both TRUS and MRI prior to radical prostatectomy at one academic institution were retrospectively analyzed. TRUS and MRI volumes were estimated using the prolate ellipsoid formula. TRUS volumes were collected from sonography reports. MRI volumes were estimated by two blinded raters and the mean of the two was used for analyses. Pathological volume was calculated using a standard fluid displacement method. RESULTS: Three hundred and eighteen (318) patients were included in the analysis. MRI was slightly more accurate than TRUS based on interclass correlation (0.83 vs. 0.74) and absolute risk bias (higher proportion of estimates within 5, 10, and 20 cc of pathological volume). For TRUS, 87 of 298 (29.2%) prostates without median lobes differed by >10 cc of specimen volume and 22 of 298 (7.4%) differed by >20 cc. For MRI, 68 of 298 (22.8%) prostates without median lobes differed by >10 cc of specimen volume, while only 4 of 298 (1.3%) differed by >20 cc. CONCLUSIONS: MRI and TRUS prostate volume estimates are consistent with pathological volumes along the prostate size spectrum. MRI demonstrated better correlation with prostatectomy specimen volume in most patients and may be better suited in cases where TRUS and MRI estimates are disparate. Validation of these findings with prospective, standardized ultrasound techniques would be helpful.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".