The diagnostic value of <scp>MRI</scp> for persistent prostate cancer following irreversible electroporation focal therapy
Bibliographic record
Abstract
OBJECTIVE: To investigate the diagnostic value of magnetic resonance imaging (MRI) for persistent prostate cancer after irreversible electroporation (IRE) therapy. PATIENTS AND METHODS: This is a post hoc analysis from a multicentre randomised trial, in which men with localised low- to intermediate-risk prostate cancer were randomised to receive either focal or extended IRE ablation. All patients underwent repeat MRI scans at 6 and 12 months and transperineal template mapping biopsy (TMB) at 6 months post-IRE. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of MRI were calculated for infield and outfield lesions using 2 × 2 contingency tables with 95% confidence intervals (CIs) for clinically significant prostate cancer and any-grade prostate cancer. RESULTS: A total of 106 patients were recruited to this study, including 39 patients (37%) with clinically insignificant prostate cancer and 67 patients (63%) with clinically significant prostate cancer (International Society of Urological Pathology grade ≥2). Of these, 101 patients underwent repeat MRI scan and prostate biopsy at 6 months after IRE. The rate of clinically significant prostate cancer detected by TMB infield and outfield was 9.9% (10/101) and 9.9% (10/101), respectively. In the treated area, the sensitivity, specificity, PPV and NPV for MRI to detect clinically significant prostate cancer were 30% (95% CI 6.7%-65%), 91% (95% CI 82%-96%), 27% (95% CI 6.0%-61%) and 92% (95% CI 84%-97%), respectively. In the untreated area, the sensitivity, specificity, PPV and NPV of MRI to detect clinically significant prostate cancer were 20% (95% CI 2.5%-56%), 91% (95% CI 82%-96%), 20% (95% CI 2.5%-56%) and 91% (95% CI 82%-96%), respectively. CONCLUSION: Favourable specificity but poor sensitivity was achieved with use of MRI to detect persistent clinically significant prostate cancer after IRE treatment. Repeat TMB should not be deferred, regardless of MRI results.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".