Renal epithelioid angiomyolipoma: a study of six cases and a meta‐analytic study. Development of criteria for screening the entity with prognostic significance
Bibliographic record
Abstract
AIMS: Renal epithelioid angiomyolipoma (EAML) is only described in case reports or in multi-institutional small series. The aim was to report cases seen at our institution and to perform a meta-analysis based on a literature review. METHODS AND RESULTS: Six EAML cases seen at our institution were reviewed and a meta-analysis performed using cases retrieved from a literature review. There were a total of 69 cases for review. The male:female ratio was 1:3. In the absence of areas of typical AML, useful features in distinguishing EAML from epithelial renal neoplasms include: extreme degree of cytological atypia, histiocytoid appearance, presence of melanocytic pigments, solid architecture with the absence of frequent areas of alveolar pattern, tubulo-papillary formation and scarring. A fatal outcome, distant or lymph node metastasis, venous invasion and local recurrence were considered as adverse events and occurred in 40% of cases over a period of follow-up of 3-60 months (mean 22.5 +/- 18 months). Tumours with an unfavourable outcome showing marked cytological atypia and extensive tumour necrosis were larger (135 +/- 43 mm) than those with a favourable outcome (79 +/- 50 mm) (P < 0.002), and predominantly occurred in men. CONCLUSIONS: Renal neoplasms with certain unusual features should be investigated immunohistochemically to rule out the possibility of EAML. The frequency of adverse outcome is lower in EAML than in renal cell carcinoma.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".