Report From the International Society of Urological Pathology (ISUP) Consultation Conference On Molecular Pathology Of Urogenital Cancers. II. Molecular Pathology of Bladder Cancer
Bibliographic record
Abstract
During the 2019 International Society of Urological Pathology Consultation Conference on Molecular Pathology of Urogenital Cancer, the Working Group on Bladder Cancer presented the current status and made recommendations on the diagnostic use of molecular pathology, incorporating a premeeting survey. Bladder cancers are biologically diverse and can be separated into "molecular subtypes," based on expression profiling. These subtypes associate with clinical behavior, histology, and molecular alterations, though their clinical utility has not been demonstrated at present and use in bladder cancer is not recommended. Mutations in the TERT promoter are present in the majority of bladder cancers, including the noninvasive stage of tumor evolution, but not in reactive conditions. Mutational analysis of the TERT promoter thus distinguishes histologically deceptive cancers from their benign mimics in some cases. A minority of pathologists employ this test. FGFR3 mutations are common in bladder cancer, and metastatic urothelial carcinoma (UC) with such mutations frequently responds to erdafitinib, an FGFR inhibitor. Testing for FGFR3 alterations is required before using this drug. Metastatic UC responds to immune-oncology (IO) agents in 20% of cases. These are approved as first and second-line treatments in metastatic UC. Several biological parameters associate with response to IO agents, including tumor mutational burden, molecular subtype, and infiltration by programmed death-ligand 1-positive lymphocytes, detected by immunohistochemistry. Programmed death-ligand 1 immunohistochemistry is mandatory before administering IO agents in the first-line setting. In conclusion, much has been learned about the biology of bladder cancer, and this understanding has improved the care of patients with the disease.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.024 |
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".