Prostate MRI Transitional Zone Volume Predicts BPH Enucleation Volume Better than Alternative Modalities
Bibliographic record
Abstract
Introduction: Guidelines for benign prostate hyperplasia (BPH) interventions are volume based. The degree to which different imaging modalities actually correlate to treated volume is not known for BPH. The present study compares the accuracy of preoperative ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and MRI-transitional zone (TZ) to BPH enucleation weight. Methods: A retrospective review of patients who underwent enucleation for BPH and had preoperative transrectal ultrasound (TRUS), CT, and/or MRI was performed. Total prostate volumes were measured for CT, MRI, and TRUS; MRI-TZ volume was also measured. The primary outcome was difference between enucleated pathology weight in grams and preoperative imaging volume. Differences between enucleation and imaging volume for each modality were calculated with one-way analysis of variance, with Tukey’s honest significance test to determine pairwise significance (RStudio V1.2). Results: From January to October 2020, there were 114 preoperative imaging studies available for 95 patients. Thirty-four (30%) of the studies were TRUS, 46 (40%) were CT, and 34 (30%) were MRI. MRI-TZ most accurately predicted enucleation volume on multivariate analysis (F-statistic p -value < 0.001). Preoperative imaging was greater than enucleation volume by a median of 46 cc for TRUS, 51 cc for CT, 53 cc for MRI, and 14 cc for MRI-TZ. Pairwise significance was reached for MRI-TZ over CT ( p -adj < 0.001), MRI-TZ over MRI ( p -adj < 0.001) and MRI-TZ over US ( p -adj = 0.03). Conclusions: Enucleation volume for BPH was most accurately predicted by TZ volume on MRI compared with total prostate volume on CT, TRUS, and MRI. MRI total volume was not superior to CT total volume. Focusing on MRI-TZ volume rather than total prostate volume may more accurately stratify patients for BPH treatment. In experienced hands, median enucleation volume is within 14 cc of MRI-TZ volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".