Non-Invasive Imaging Modalities in Intravesical Murine Models of Bladder Cancer
Bibliographic record
Abstract
Bladder cancer (BCa) is the sixth most prevalent cancer in men and seventeenth most prevalent cancer in women worldwide. Current treatment paradigms have limited therapeutic impact, suggesting an urgent need for the investigation of novel therapies. To best emulate the progression of human BCa, a pre-clinical intravesical murine model is required in conjunction with existing non-invasive imaging modalities to detect and evaluate cancer progression. Non-invasive imaging modalities reduce the number of required experimental models while allowing for longitudinal studies of novel therapies to investigate long-term efficacy. In this review, we discuss the individual and multi-modal use of non-invasive imaging modalities; bioluminescence imaging (BLI), micro-ultrasound imaging (MUI), magnetic resonance imaging (MRI), and positron emission tomography (PET) in BCa evaluation. We also provide an update on the potential and the future directions of imaging modalities in relation to intravesical murine models of BCa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".