Sensitivity and Specificity of Diffusion-Weighted Magnetic Resonance Imaging in Diagnosis of Bladder Cancers
Bibliographic record
Abstract
PURPOSE: Our study is designed to examine the diagnostic performance of diffusion-weighted magnetic resonance imaging (DW-MRI) for bladder cancers (BC), and to determine whether DW-MRI can differentiate muscle invasive bladder cancer (MIBC) from non-MIBC (NMIBC). METHODS: A meta-analysis was performed of published studies that investigated the performance of DW-MRI for BC. These studies were retrieved from scientific literature databases using sensitive electronic search strategies. The STATA 12.0 and Meta-disc software were employed for statistical analyses of data extracted from selected studies. RESULTS: Our search initially returned 230 articles, of which 11 met the inclusion criteria and were enrolled into the final meta-analysis. Five of the included studies reported the diagnostic performance of DW-MRI for BC with a cumulative total of 243 BC patients and 82 healthy subjects. Eight studies investigated the diagnostic performance of DW-MRI for differentiating MIBC from NMIBC, involving 259 MIBC lesions and 515 NMIBC lesions. Meta-analysis results were as follows: the diagnostic performance of DW-MRI for BC (sensitivity: 0.95 [0.75-0.99]; specificity: 0.85 [0.74-0.92]; positive likelihood ratio: 6.45 [3.64-11.42]; negative likelihood ratio: 0.055 [0.009-0.333]; diagnostic odds ratio: 117.11 [19.37-708.05]; area under the curve (AUC): 0.91); the diagnostic performance of DW-MRI to differentiate MIBC from NMIBC (sensitivity: 0.85 [0.76 - 0.91]; specificity: 0.90 [0.87 - 0.93]; positive likelihood ratio:8.81[6.43 - 12.07]; negative likelihood ratio: 0.16 [0.10 - 0.28]; diagnostic odds ratio: 53.95 [25.68 - 113.33]; AUC: 0.92). CONCLUSION: DW-MRI has an outstanding diagnostic performance, with advanced sensitivity and specificity, for imaging of bladder cancers and for differentiating MIBC from NMIBC.
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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 | 0.037 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".