Transesophageal endoscopic ultrasound fine needle aspiration of vertebral body osteolytic tumors – a novel diagnostic approach. Case series.
Bibliographic record
Abstract
AIMS: Vertebral lesions, either primary or more frequently metastasis, are difficult targets for percutaneous guided biopsies and surgical biopsies and are associated with greater risks of complications. We investigated the feasibility of endoscopic ultrasound (EUS) fine needle aspiration (FNA) biopsy in the assessment of vertebral osteolytic tumors as an alternative to CT guided biopsy which is the technique currently used. MATERIAL AND METHODS: Four patients with osteolytic tumors of the vertebral bodies identified by imaging methods (CT or MRI) - 3 patients, and one with a tumor detected primarily during EUS procedure were included in order to evaluate the feasibility of the procedure. The lesions were located either at the dorsal or lumbar vertebrae. In all cases we performed EUS FNA of the osteolytic vertebral body lesions with 22G needles using the transesophageal or transgastric approach. RESULTS: In all cases EUS FNA provided enough tissue for an accurate histopathological report, with no procedural complication. We diagnosed lung adenocarcinoma, hepatocarcinoma and a pancreatic adenocarcinoma vertebral metastasis and one case of lymphoma. CONCLUSIONS: EUS FNA is a valuable technique which should be considered in selected cases, when a "traditional approach" is not applicable or associated with a higher risk. Treatment guidelines are based on the histology of the tumor, histopathological examination being nowadays mandatory. Therefore, we propose for selected cases a feasible technique, with significantly lower procedural risks, as an alternative for open surgical biopsies or computed tomography guided biopsies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".