Emerging treatment options for bacillus Calmette–Guérin-unresponsive non-muscle invasive bladder cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: The majority of new bladder cancer diagnoses are non-muscle invasive bladder cancer (NMIBC). For patients with intermediate-risk and high-risk NMIBC, the preferred treatment after transurethral tumor resection is bacillus Calmette-Guérin (BCG) intravesical therapy; however, some patients receiving BCG do not respond and are at risk for recurrence and progression to muscle invasive disease. Currently, there is a lack of bladder-sparing therapies for patients that do not respond to BCG therapy. This review highlights recent advances in bladder-sparing therapies for NMIBC that do not respond to BCG therapy. RECENT FINDINGS: Several clinical trials have emerged over the past several years using novel agents. Systemic immunotherapy with pembrolizumab has shown promising activity, and recently gained FDA approval for use in BCG-unresponsive NMIBC. Gene therapy using Nadofaragene firadenovec, a replication-deficient adenoviral vector delivering interferon (IFN) alfa, shows durable results and is pending regulatory approval. Several other agents are on the horizon and there will likely be an emergence of therapies over the next several years. SUMMARY: Patients with NMIBC that do not respond to BCG therapy are at high risk for progression to muscle invasive disease. Bladder-sparing therapeutic options for these patients are sparse but now include pembrolizumab and several other potential options are emerging. These bladder-sparing therapies will benefit patients unwilling or unable to undergo radical cystectomy.
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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 | 0.001 | 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) | 0.005 | 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".