Do Prostate Imaging‐Reporting and Data System (PIRADS) lesions predict holmium laser enucleation of prostate outcomes?
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Prostate magnetic resonance imaging (MRI) is used for prostate cancer (PCa) screening and risk stratification and is helpful for surgical planning for patients undergoing holmium laser enucleation of the prostate (HoLEP). There are few studies investigating the correlation between MRI Prostate Imaging-Reporting and Data System (PIRADS) lesion characteristics and HoLEP pathology and outcomes. METHODS: We performed retrospective review of patients who underwent HoLEP between January 2021 and August 2023 by a single surgeon. Preoperative, intraoperative, and postoperative characteristics and outcomes were analyzed for all patients who had a documented preoperative prostate MRI. RESULTS: There were 334 patients without a pre-existing diagnosis of PCa and with a preoperative prostate MRI, of which 140 (42%) had at least one PIRADS lesion. There was a total of 203 PIRADS lesions: 91 (45%) in the peripheral zone (PZ), 106 (52%) in the transition zone (TZ), and 6 (2%) not specified. Incidental PCa was noted in 44 (13%) patients at time of HoLEP. Presence or location of lesion was not significantly associated with rate or grade of incidental PCa on pathology. Greater number of lesions and lesion size correlated with longer procedure times. Lesion number, size, or grade were not found to correlate with cancer grade or rate of cancer. CONCLUSIONS: Grade, presence, location, size, and number of PIRADS lesions on preoperative prostate MRI for patients with an appropriate prior PCa workup were not significantly associated with incidental PCa or higher PCa grade on HoLEP pathology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".