Renal Leiomyoma
Bibliographic record
Abstract
Renal leiomyoma is an exceptionally rare benign mesenchymal tumor of the kidney predominantly arising in proximity of the renal capsule or pelvis. Its rarity and nonspecific clinical and imaging features may lead to radical or partial nephrectomy on the basis of preoperative suspicion of renal cell carcinoma. The diagnosis of renal leiomyoma is challenging because of the histologic overlap with lipid-poor angiomyolipoma (AML). We conducted a multi-institution study to characterize renal leiomyoma in greater detail. We collected and reviewed 24 cases diagnosed initially as renal leiomyoma in 10 institutions from North America, Canada, and Europe. Immunohistochemical expression of desmin, HMB-45, estrogen receptor (ER), progesterone receptor (PR), and cathepsin K was evaluated. Upon central review, 9 tumors were classified as renal leiomyoma, whereas the remaining were reclassified as AML (n=13), myolipoma (n=1), and medullary fibroma (n=1). All renal leiomyomas were solitary and occurred in female patients (mean age 63 y; range, 44 to 74 y). Tumor size ranged from 0.6 to 7.0 cm (mean 2.9 cm); 7 originated from the renal capsule or the subcapsular area and 1 from a large vessel in the renal sinus. All leiomyomas were diffusely positive for desmin and negative for HMB-45 and cathepsin K; 6/9 (67%) showed diffuse ER and PR expression, and 1 case showed focal ER positivity only. Renal leiomyoma should be included in the histologic differential diagnosis of solid renal masses, particularly in perimenopausal women. The main differential diagnosis is with lipid-poor AML, and cathepsin K plays a key role in distinguishing these 2 lesions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".