Diagnostic Performance of Magnetic Resonance Imaging for Preoperative Local Staging of Penile Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Invasion of the tunica albuginea (TA) and/or urethra are key factors in determining the feasibility of organ-preserving surgery in penile cancer (PC). Magnetic resonance imaging (MRI) appeared to be a promising technique for preoperative local staging. We performed a systematic review (SR) and pooled meta-analysis to investigate the diagnostic performance of MRI in preoperative local staging of primary PC. An SR up to May 2021 was performed according to the PRISMA statement. The diagnostic performance of MRI was evaluated according to TA invasion, urethra invasion, and pT-stage ≥ 2. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) from eligible studies were pooled and summary receiver operating characteristic (SROC) curves were constructed. Overall, seven qualified studies were deemed suitable. Diagnostic performance of MRI showed an accuracy of 0.89 for TA invasion (sensitivity 0.78, PPV 0.79, specificity 0.91, and NPV 0.90); an accuracy of 0.88 for urethra invasion (sensitivity 0.65, PPV 0.46, specificity 0.86, and NPV 0.93); an accuracy of 0.90 for pT ≥ 2 (sensitivity 0.86, PPV 0.84, specificity 0.70, and NPV 0.73).Currently available evidence indicates that MRI might be a one-stop shop for local staging of primary PC and play a central role with regard to conservative surgical management.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".