The Role of Magnetic Resonance Imaging in Penile Fracture Management—A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Penile fractures are a rare urological emergency, defined as the traumatic rupture of the tunica albuginea. They are classically diagnosed on clinical grounds, requiring urgent operative repair, most commonly by penile degloving. Magnetic resonance imaging (MRI) has emerged as a promising tool in the management of penile fractures. Often recommended in the setting of equivocal clinical diagnoses, MRI can help diagnose, as well as localise, the site of injury. Furthermore, it also holds potential in differentiating penile fractures from mimicking conditions, thereby possibly preventing unnecessary surgical procedures. This study is aimed at evaluating the diagnostic accuracy of MRI for penile fractures. Furthermore, it seeks to explore MRI’s effectiveness in guiding the surgical approach through precise localisation of the injury site. Methods: The PubMed, Embase, and Cochrane databases were searched from January 1995 to December 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and a total of 246 cases from 32 studies were identified. Results: Pooled results for MRI use in penile fracture diagnosis resulted in a positive predictive value of 97.8%, a negative predictive value of 87.0%, a sensitivity of 98.6%, and a specificity of 80%. MRI can accurately guide localised incisions, due to its ability to accurately identify the exact site of injury, with no additional reported complications or conversions to degloving. Considerable heterogeneity was observed within MRI parameters and protocols used in the studies identified. Conclusions: This review suggests that MRI is an accurate imaging modality for penile fractures and should be considered as a first-line investigation for equivocal cases. Its application may refine clinical management by avoiding unnecessary surgeries in cases mimicking penile fractures and improve pre-operative planning through precise injury localisation. This study is limited by heterogeneity in MRI protocols and the small sample sizes and retrospective nature of many included studies. The future standardisation of MRI protocols could enhance its utility and reliability in the clinical setting. Additionally, further research is needed to evaluate the long-term outcomes following the repair of small fractures detected on MRI and following MRI-guided localised incisions during surgical repair. Level of Evidence: 2.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".