Diagnostic accuracy of magnetic resonance imaging for tumour staging of bladder cancer: systematic review and meta‐analysis
Bibliographic record
Abstract
The purpose of this study is to evaluate accuracy of magnetic resonance imaging (MRI) for local staging of bladder cancer for four clinical scenarios (T-stage thresholds) considered against current standards for clinical staging and secondarily to identify sources for variability in accuracy. Systematic review of patients with bladder cancer undergoing T-staging MRI to evaluate the diagnostic accuracy using bivariate random-effects meta-analysis. Sub-group analysis was done to explore variability; risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. The search identified 30 studies (5156 patients). Pooled accuracy at multiple T-stage thresholds: ≤T1 vs ≥T2 = sensitivity 87% (95% confidence interval [CI] 82-91), specificity 79% (95% CI 72-85); T-any vs T0 = sensitivity 65% (95% CI 23-92), specificity 90% (95% CI 83-94); ≤T2 vs ≥T3 = sensitivity 83% (95% CI 75-88), specificity 87% (95% CI 78-93); and <T4b vs pT4b = sensitivity 85% (95% CI 63-95), specificity 98% (95% CI 95-99). For ≤T1 vs ≥T2, accuracy was higher in studies at low risk of bias. No variability in accuracy was identified for: field strength, transurethral resection of bladder tumour status, publication date, index test parameters. For ≤T1 vs ≥T2, accuracy was higher than reported for clinical staging. For T-any vs T0 accuracy was lower than clinical staging. For ≤T2 vs ≥T3, sensitivity was slightly lower than clinical staging but specificity was considerably higher. For <T4b vs pT4b sensitivity exceeded the estimated accuracy for clinical staging. Limitations: two scenarios had few studies (T-any vs T0; <T4b vs pT4b) and several studies were at high risk of bias. MRI staging for ≤T1 vs ≥T2, ≤T2 vs ≥T3, and
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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 | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| 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) | 0.002 | 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".