BCG and Alternative Therapies to BCG Therapy for Non-Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Bladder cancer is a heterogeneous disease. Treatment decisions are mostly decided based on disease stage (non-muscle invasive or muscle invasive). Patients with muscle-invasive disease will be offered a radical treatment combined with systemic therapy, while in those with non-muscle-invasive disease, an attempt to resect the tumor endoscopically will usually be followed by different intravesical instillations. The goal of intravesical therapy is to decrease the recurrence and/or progression of the tumor. In the current landscape of bladder cancer treatment, BCG is given intravesically to induce an inflammatory response and recruit immune cells to attack the malignant cells and induce immune memory. While the response to BCG treatment has changed the course of bladder cancer management and spared many "bladders", some patients may develop BCG-unresponsive disease, leaving radical surgery as the best choice of curative treatment. As a result, a lot of effort has been put into identifying novel therapies like systemic pembrolizumab and Nadofaragene-Firadenovac to continue sparing bladders if BCG is ineffective. Moreover, recent logistic issues with BCG production caused a worldwide BCG shortage, re-sparking interest in alternative BCG treatments including mitomycin C, sequential gemcitabine with docetaxel, and others. This review encompasses both the historic and current role of BCG in the treatment of non-muscle-invasive bladder cancer, revisiting BCG alternative therapies and reviewing the novel therapeutics that were approved for the BCG-unresponsive stage or are under active investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".