Undifferentiated and dedifferentiated urological carcinomas: lessons learned from the recent developments
Bibliographic record
Abstract
Loss of the morphological and immunophenotypic characteristics of a neoplasm is a well-known phenomenon in surgical pathology and occurs across different tumor types in almost all organs. This process may be either partial, characterized by transition from well differentiated to undifferentiated tumor component (=dedifferentiated carcinomas) or complete (=undifferentiated carcinomas). Diagnosis of undifferentiated carcinoma is significantly influenced by the extent of sampling. Although the concept of undifferentiated and dedifferentiated carcinoma has been well established for other organs (e.g. endometrium), it still has not been fully defined for urological carcinomas. Accordingly, undifferentiated/ dedifferentiated genitourinary carcinomas are typically lumped into the spectrum of poorly differentiated, sarcomatoid, or unclassified (NOS) carcinomas. In the kidney, dedifferentiation occurs across all subtypes of renal cell carcinoma (RCC), but certain genetically defined RCC types (SDH-, FH- and PBRM1- deficient RCC) seem to have inherent tendency to dedifferentiate. Histologically, the undifferentiated component displays variable combination of four patterns: spindle cells, pleomorphic giant cells, rhabdoid cells, and undifferentiated monomorphic cells with/without prominent osteoclastic giant cells. Any of these may occasionally be associated with heterologous mesenchymal component/s. Their immunophenotype is often simple with expression of vimentin and variably pankeratin or EMA. Precise subtyping of undifferentiated (urothelial versus RCC and the exact underlying RCC subtype) is best done by thorough sampling supplemented as necessary by immunohistochemistry (e.g. FH, SDHB, ALK) and/ or molecular studies. This review discusses the morphological and molecular genetic spectrum and the recent develoments on the topic of dedifferentiated and undifferentiated genitourinary carcinomas.
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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 | 0.005 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".