Pseudo solid-appearing pancreatic serous microcystic adenomas: Histologic diagnosis with the EUS core biopsy fork-tip needle
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite rarely, serous cystic adenoma (SCA) can assume a pseudo-solid aspect mimicking other pancreatic neoplasm as neuroendocrine tumor. EUS-FNA cytology has low diagnostic accuracy due to the scant cellularity of the collected samples. Histological diagnosis is usually made after resection. Recently, end-cutting needles for EUS-fine-needle biopsy (EUS-FNB), which obtain tissue cores by penetrating the lesions, have been developed. We aimed to assess the capability of EUS-FNB with SharkCore™ needles in the preoperative diagnosis of serous cystic adenoma pseudo-solid-appearing on imaging (Sa-SCA). MATERIALS AND METHODS: Between January 2016 and January 2018, data from consecutive adult patients, who were referred for EUS-FNB of a solid pancreatic lesion and were diagnosed with having SCA, were retrieved from a single-center institutional database. RESULTS: fluoro-deoxyglucose positron emission tomography. EUS-FNB samples were judged adequate for a definitive diagnosis in all cases, achieving specimens suitable for histological evaluation and several ancillary stains. Histochemical positivity for periodic acid-Schiff (PAS) and PAS with diastase digestion was observed in 7/7 cases. Immunohistochemical positivity for α-inhibin (7/7), GLUT1 (6/6), MUC6 (5/5), and negativity for synaptophysin (7/7) and chromogranin A (2/2) favored SCA diagnosis. CONCLUSIONS: In the case of preoperative workup suspected for Sa-SCA, a "forward acquiring" needle could improve the rate of preoperative histological diagnosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".