Update on multiparametric MRI of urinary bladder cancer
Bibliographic record
Abstract
While many institutions perform MRI during the work‐up of urinary bladder cancer, others use MRI rarely if at all, possibly due to a variation in the reported staging accuracy and unfamiliarity with the potential benefits of performing MRI. Through increased application of functional imaging techniques including diffusion‐weighted imaging (DWI) and dynamic contrast‐enhanced (DCE) imaging, there has been a resurgence of interest regarding evaluation of bladder cancer with MRI. Several recent meta‐analyses have shown that MRI is accurate at differentiating between ≤T1 and T2 disease (with pooled sensitivity/specificity of ∼90/80%) and differentiating between T2 and ≥T3 disease. DWI and DCE, in combination with high‐resolution T 2 ‐weighted images, improves detection and possibly local staging accuracy of bladder cancer. High b value echo‐planar DWI is particularly valuable for tumor detection. Zoomed field of view and segmented readout DWI techniques improve image quality by reducing susceptibility artifact, while methods to extract calculated high b value images save time and improve the contrast‐to‐noise ratio. DCE traditionally required imaging of the pelvis with high temporal but lower spatial resolution; however, advances in parallel and keyhole imaging techniques can preserve spatial resolution. The use of compressed sensing reconstruction may improve utilization of DCE of the bladder, especially when imaging the abdomen simultaneously, as in MR urography. Quantitative imaging analysis of bladder cancer using pharmacokinetic modeling of DCE, apparent diffusion coefficient values, and texture analysis may enable radiomic assessment of bladder cancer grade and stage. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:882–896.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".