Role of magnetic resonance imaging in bladder cancer: current status and emerging techniques
Bibliographic record
Abstract
What's known on the subject? and What does the study add? According to current treatment guidelines, magnetic resonance imaging (MRI) or computed tomography (CT) can be used to assist in staging bladder cancer patients being considered for radical surgery. In this article, we review the evidence supporting the use of MRI for bladder cancer staging. The ability of MRI to differentiate non‐muscle invasive from muscle‐invasive bladder cancer, to differentiate organ‐confined from non‐organ‐confined bladder cancer, and to identify lymph node metastases is described in detail. Additionally, the role of MRI as a biomarker of chemotherapeutic response in bladder cancer is reviewed and summarized. OBJECTIVES • To evaluate the current status of magnetic resonance imaging (MR) as a staging tool for bladder cancer. • To investigate the role of MR in assessing chemotherapeutic response in bladder cancer patients. PATIENTS AND METHODS • A Pubmed/MEDLINE search was conducted to identify original articles, review articles, and editorials regarding the use of MR in bladder cancer. RESULTS • Contrast‐enhanced MR and diffusion weighted MR (DW‐MRI) can likely distinguish between non‐muscle invasive bladder cancer and muscle invasive cancer with >80% accuracy. • Some advantages of DW‐MRI are the differentiation of benign versus malignant tissue involvement without the need for intravenous contrast, and the possibility of obtaining information on histologic grade and T stage. • Traditional MR sequence have low sensitivity for identifying small lymph node metastases but MR lymphography (MRL) using ultra‐small paramagnetic iron oxide (USPIO) may enhance their detectin. • There may be a role for DW‐MRI in the evaluation of chemotherapeutic response in bladder cancer patients. CONCLUSION • To date, sample sizes and study designs are insufficient to clearly establish the role of MR in bladder cancer management, and to this end, well designed prospective trials are needed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".