Association of surgical margin positivity with preoperative mpMRI-identified index lesions in radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer remains the second most common malignancy in men worldwide. Positive surgical margins (PSMs) following radical prostatectomy are associated with an increased risk of biochemical recurrence. This study investigated the relationship between preoperative multiparametric magnetic resonance imaging (mpMRI)-detected index lesions and PSMs, aiming to assess whether specific lesion locations correlate with margin involvement. METHODS: A retrospective cohort study was conducted at Health Sciences University Haseki Training and Research Hospital, analyzing 148 patients who underwent radical prostatectomy between 2017 and 2023. Patients were stratified based on surgical margin status, with comparisons made between mpMRI features, pathologic outcomes, and the anatomical distribution of PSMs. Binary logistic regression was used to identify independent predictors of PSMs. RESULTS: Of the 148 patients, 49 had PSMs. Higher preoperative prostate-specific antigen levels, prostate-specific antigen density, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were significantly associated with PSMs. Multivariate analysis revealed that PI-RADS 5, International Society of Urological Pathology grade 4 or above, and extraprostatic extension were independent predictors of PSMs. Although lesions in the apical and posterior regions showed higher rates of PSMs, the regional differences were not statistically significant. CONCLUSIONS: Our findings suggest that mpMRI plays a critical role in preoperative risk stratification and may guide surgical planning to reduce PSMs; however, further prospective studies are needed to validate these results and explore the potential benefits of targeted resections in high-risk regions for improving oncologic outcomes.
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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 | 0.000 | 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) | 0.002 | 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".