Multiparametric magnetic resonance imaging for bladder cancer: a comprehensive systematic review of the Vesical Imaging-Reporting and Data System (VI-RADS) performance and potential clinical applications
Bibliographic record
Abstract
BACKGROUND: The Vesical Imaging-Reporting and Data System (VI-RADS) score is a novel standardized approach to image and report bladder cancer (BC) with multiparametric MRI (mpMRI). OBJECTIVES: To describe and evaluate the performance of the VI-RADS score using mpMRI and assess its potential clinical applications and limitations. METHODS: A systematic review was conducted using the MEDLINE and EMBASE electronic bibliographic databases between June 2020 and December 2020. All reports deemed relevant to describe the VI-RADS score and assess its performance and applications were retrieved. Results presentation stands as narrative, purely descriptive synthesis based on aggregate studies data. RESULTS: A total of 20 relevant studies were retrieved: three meta-analyses, five prospective studies, and twelve retrospective studies. The retrospective studies covered 1676 patients, while the prospective studies included a total number of 468 patients. Pooled sensitivity, specificity to differentiate muscle-invasive from non-muscle-invasive bladder cancer, ranged from 74.1% to 97.3%, and 77% to 100%, respectively. The chosen VI-RADS score thresholds for this discrimination varied across studies. The interreader agreement ranged from 0.73 to 0.95. Currently, the potential clinical applications of VI-RADS consist of initial BC risk stratification, assessment of neoadjuvant therapies response, and bladder sparing approaches, although further validation is required. CONCLUSIONS: The VI-RADS score helps to discriminate muscle invasive from non-muscle invasive BC with good performance and reproducibility. A simple algorithm based on four basic questions may enhance its popularization. Further studies are required to validate the clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".