Diagnostic Accuracy of Contrast-Enhanced Ultrasound (CEUS) in the Detection of Muscle-Invasive Bladder Cancer: A Systematic Review and Diagnostic Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Contrast-enhanced ultrasound (CEUS) is a diagnostic tool that is gaining popularity for its ability to improve overall diagnostic accuracy in bladder cancer (BC) staging. Our aim is to determine the cumulative diagnostic performance of CEUS in predicting preoperative muscle invasiveness using a comprehensive systematic review and pooled meta-analysis. METHODS: A systematic review until October 2023 was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Patients with BC suspicion were offered CEUS before the transurethral resection of the bladder tumor (TURBT). The diagnostic performance of CEUS was evaluated based on non-muscle-invasive bladder cancer (NMIBC) vs. muscle-invasive bladder cancer (MIBC) confirmed at the final histopathological examination after TURBT. The outcomes were determined through pooled sensitivity, specificity, pooled positive likelihood ratio (PLR+), negative likelihood ratio (PLR-), and area under the summary receiver operating characteristic (SROC) along with their respective 95% confidence intervals (CI). RESULTS: Overall, five studies were included. In these studies, a total of 362 patients underwent CEUS prior to TURBT. The pooled sensitivity and specificity were 0.88 (95% CI: 0.81-0.93) and 0.88 (95% CI: 0.82-0.92), respectively. SROC curve depicted a diagnostic accuracy of 0.94 (95% CI: 0.81-0.98). The pooled PLR+ and PLR- were 7.3 (95% CI: 4.8-11.2) and 0.14 (95% CI: 0.08-0.23), respectively. CONCLUSIONS: Our meta-analysis indicates that CEUS is highly accurate in the diagnosis and staging for BC. Beyond its accuracy, CEUS offers the advantage of being a cost-effective, safe, and versatile imaging tool.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".