Endoscopic ultrasound-guided trans-duodenal fine-needle biopsy of a small renal mass: case report and review of the literature
Bibliographic record
Abstract
Abstract Background The incidental detection of small renal masses (SRMs) is increasing and biopsy to obtain pathological diagnosis is increasingly proposed as a diagnostic tool to guide further management. Renal mass biopsies are traditionally performed via a percutaneous approach. However, this is not always feasible due to anatomical limitations. A rarely reported alternative biopsy approach for SRMs is endoscopic ultrasound (EUS)-guided fine-needle biopsy (FNB). Herein, we describe a case of EUS-guided trans-duodenal FNB for a SRM that was not amenable to standard percutaneous biopsy. Case presentation A 48-year-old man was incidentally found to have a right-sided SRM measuring 2.9 × 2.2 × 2.4 cm during evaluation for a hernia. It was anterior, interpolar, completely endophytic and near the renal hilum. The tumor was not amenable to traditional percutaneous biopsy due to its anterior location. However, the renal mass was in close proximity to the descending duodenum and so it was felt that an EUS-guided trans-duodenal FNB would be feasible. The procedure was successful without any complications. The specimen adequacy was satisfactory for evaluation and consistent with renal papillary carcinoma with WHO/ISUP grade 3 nuclear changes. Conclusion Our case report demonstrated that EUS-guided trans-duodenal FNB was a safe and feasible approach to obtaining biopsy tissue diagnosis of a SRM that was not amenable to percutaneous biopsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".