Surgeon-controlled robotic partial nephrectomy for a rare renal epitheliois angiomyolipoma using near-infrared fluorescence imaging using indocyanine green dye: A case report and literature review
Bibliographic record
Abstract
Renal epithelioid angiomyolipoma (E-AML) is a rare variant of angiomyolipoma (AML). It is a mesenchymal tumour believed to originate from the perivascular epithelioid cell (PEC). Unlike conventional AML which are benign, E-AML has a rare aggressive behaviour. Conventional AML is typically triphasic containing adipose tissue, smooth muscle and dystrophic vessels in variable proportions, while E-AML are generally composed of plump spindled and polygonal-shaped "epithelioid cells" showing clear or eosinophilic cytoplasm and occasional pleomorphic multinucleated giant cells. E-AML can be misdiagnosed as renal cell carcinoma (RCC) when these "epithelioid cells" show clearing. Only a small number of cases of E-AML have been reported with the standard treatment being radical or partial nephrectomy. We report the first case report of a surgeon-controlled robotic partial nephrectomy using a near-infrared fluorescence imaging using indocyanine green dye on a 25-year-old woman with a T1B (6.6 cm) right renal mass. The final pathology revealed the diagnosis of E-AML. There was no recurrence and metastases after the 6-month follow-up.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".