Through-The-Needle Biopsy: Shifting From Cytology to Histology for Preoperative Assessment of Pancreatic Cystic Lesions
Bibliographic record
Abstract
To the Editor.—We congratulate Michelle Reid, MD, on her exhaustive and comprehensive review on fluid cytology for assessment of pancreatic cystic lesions (PCLs).1Traditionally, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) with cytologic evaluation has been the method of choice for diagnosis of PCL. However, disappointing results of its diagnostic yield have been reported, due to the lack of dispersed cells into the fluid.2At the beginning of her article, Reid1 mentions that, to improve preoperative assessment of PCL, both the ancillary tests to be performed on cyst fluid (eg, molecular diagnostics) and new technologies for tissue collection have been developed. In particular, a microforceps (Moray Microforceps, US Endoscopy, Mentor, Ohio) able to perform a biopsy of the cyst wall passing through a 19G needle has been recently introduced. However, we believe that through-the-needle biopsy (TTNB) is not a simple implementation of EUS-FNA but a breakthrough in the field that deserves more comment. TTNB allows the acquisition of histologic specimens from the cyst wall, which include both stroma and epithelium, a possibility that had never been realizable in the past. The presence of stroma, as well as the maintained tissue architecture of the cystic wall detected on TTNB specimens, is crucial for the differential diagnosis in many cases.A comprehensive example of the diagnostic yield of TTNB is seen with mucinous cyst lesions in which the capability of the microforceps to sample the ovarian-like stroma beneath the mucinous epithelium allows the conclusive diagnosis of mucinous cystic neoplasms, a cyst histotype requiring specific management.3 Available literature speaks in favor of TTNB compared with cytology. A meta-analysis including 454 patients demonstrated a technical success rate of 98.5%, a histologic rate of 86.7%, and a diagnostic yield of approximately 70%,4 significantly higher than that of cytology (approximately 29%). Importantly, in an interobserver agreement study among expert pathologists, a specific diagnosis of cyst histotype was reported in 84.7% of cases, with a substantial agreement between the raters (Gwet AC1 coefficients, 0.62; 95% CI, 0.57–0.67),5 suggesting that TTNB samples are not misleading and are easily evaluable. Also, TTNB findings seem reliable when compared with surgical specimens, with a correspondence rate with surgical pathology of 93%.4 Finally, TTNB is feasible even when fluid aspiration is not possible owing to thick cyst content. Recently, a helpful algorithm has been proposed by Rift et al6 in which, in the presence of epithelium and/or stroma on initial evaluation of the biopsy sample, different immunohistochemical stains can be added to establish a diagnosis. Of course, clinical suspicion based on patient demographics and lesion imaging features should be also considered. A similar policy is applied at our institution.We commend Dr Reid for her wonderful article, which is complete and comprehensive, and serves as a guide for pathologists for such a difficult evaluation as PCL cytology. However, even if cytology still represents a widespread method for PCL evaluation, we believe that TTNB should be performed in select cases, especially when a specific diagnosis of cyst histotype is required for proper patient management.
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 | 0.003 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".