Immunohistochemical biomarkers for bladder cancer prognosis
Bibliographic record
Abstract
Urothelial carcinoma of the bladder (UCB) is an especially complex and heterogeneous disease with a broad spectrum of histologic findings and potentially lethal behavior. Despite advances in surgical techniques, as well as intravesical and systemic therapies, up to 30% of patients with non-muscle-invasive UCB and 50% of patients with muscle-invasive UCB experience disease progression, recurrence, and eventual death. Standard prognostic features, such as pathologic stage and grade, have limited ability to predict the outcomes of this heterogeneous population. Current risk-stratification algorithms using clinical and pathologic parameters are limited in their prognostic ability. Molecular medicine holds the promise that clinical outcomes will be improved by more accurate prognostication and directing therapy towards the mechanisms and targets associated with the growth of an individual patient's tumor. Immunohistochemical analysis of biomarker expression has provided insight into the molecular pathogenesis of UCB and offers the potential for improving clinical decision making. Numerous candidate immunohistochemical biomarkers for patients with UCB have been identified, with those relating to the cell cycle and apoptosis/cell proliferation being the most extensively studied. The present review discusses the most promising immunohistochemical biomarkers. Special attention is paid to recent data from a multi-institutional collaboration that has implemented a regulated, phased biomarker discovery and validation pathway. Because UCB tumorigenesis and progression is a process involving multiple genetic and epigenetic alterations, multiple biomarkers need to be integrated into a prognostic signature to accurately predict outcomes. There is no doubt that biomarkers will eventually guide our clinical decision making regarding follow-up scheduling and treatment choice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".