What’s new in the WHO 2022 classification of kidney tumours?
Bibliographic record
Abstract
tumours (5th edition) has significantly improved our understanding of the morphologic, immunohistochemical, and molecular characteristics of renal tumours. The aim of this review is to outline the most important changes and diagnostic updates in the WHO 2022 classification of kidney tumours. A major change in this edition is the grouping of renal tumours into broader categories that include “clear cell renal tumours”, “papillary renal tumours”, “oncocytic and chromophobe renal tumours”, “collecting duct tumours” as well as adding two categories of “other renal tumours” and “molecularly defined renal carcinomas”. Novel entities included in the WHO 2022 classification are eosinophilic solid and cystic renal cell carcinoma (ESC RCC), anaplastic lymphoma kinase (ALK)-rearranged RCC and ELOC (formerly TCEB1)-mutated RCC. The category of “other renal tumours” includes a group of diverse, unrelated renal tumours that do not fit into other categories. The group of “molecularly defined renal carcinomas” reflects recent discoveries in the renal tumour genomics. These molecularly-defined renal entities demonstrate a set of morphologic features reflecting genotype-phenotype relationships. Final diagnosis of such entities rests on phenotypic and immunohistochemical (IHC) correlation, usually associated with IHC surrogate makers that reflect specific genetic abnormalities.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".