Comprehensive Insights Into Renal Perivascular Epithelioid Cell Neoplasms: From Molecular Mechanisms to Clinical Practice
Bibliographic record
Abstract
Perivascular epithelioid cell neoplasms (PEComas) are a rare category of mesenchymal tissue tumors, manifesting across various tissues and organs such as the kidneys, liver, lungs, pancreas, uterus, ovaries, and gastrointestinal tract. They predominantly affect females more than males. PEComas characteristically express both melanocytic and smooth muscle markers, making immunohistochemistry vital for their diagnosis. Renal angiomyolipoma (AML) represents a common variant of PEComas, typically marked by favorable prognoses. Nonetheless, only a small fraction of subtypes, especially epithelioid AML, possess the capacity to be malignant. Renal PEComas usually appear as asymptomatic masses accompanied by vague imaging characteristics. The main methods for diagnosis are histopathological analysis and the application of immunohistochemical stains. Presently, a uniform treatment plan for renal PEComas is absent. Strategies for management include active surveillance, selective arterial embolization, surgical procedures, and drug-based treatments. The focus of this review is on renal PEComas, shedding light on their pathogenesis, pathological characteristics, clinical presentations, diagnosis, and treatment modalities, and incorporating a clinical case study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".