ESC, ALK, HOT and LOT: Three Letter Acronyms of Emerging Renal Entities Knocking on the Door of the WHO Classification
Bibliographic record
Abstract
Kidney neoplasms are among the most heterogeneous and diverse tumors. Continuous advancement of this field is reflected in the emergence of new tumour entities and an increased recognition of the expanding morphologic, immunohistochemical, molecular, epidemiologic and clinical spectrum of renal tumors. Most recent advances after the 2016 World Health Organization (WHO) classification of renal cell tumors have provided new evidence on some emerging entities, such as anaplastic lymphoma kinase rearrangement-associated RCC (ALK-RCC), which has already been included in the WHO 2016 classification as a provisional entity. Additionally, several previously unrecognized entities, not currently included in the WHO classification, have also been introduced, such as eosinophilic solid and cystic renal cell carcinoma (ESC RCC), low-grade oncocytic renal tumor (LOT) and high-grade oncocytic renal tumor (HOT) of kidney. Although pathologists play a crucial role in the recognition and classification of these new tumor entities and are at the forefront of the efforts to characterize them, the awareness and the acceptance of these entities among clinicians will ultimately translate into more nuanced management and improved prognostication for individual patients. In this review, we summarise the current knowledge and the novel data on these emerging renal entities, with an aim to promote their increased diagnostic recognition and better characterization, and to facilitate further studies that will hopefully lead to their formal recognition and consideration in the future classifications of kidney tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".