Ultrasound-Enhanced Fine-Needle Biopsy Improves Yield in Human Epithelial and Lymphoid Tissue
Bibliographic record
Abstract
OBJECTIVE: Needle biopsy is a common technique used to obtain cell and tissue samples for diagnostics. Currently, two biopsy methods are widely used: (i) fine-needle aspiration biopsy (FNAB) and (ii) core needle biopsy (CNB). However, these methods have limitations. Recently, we developed ultrasound-enhanced fine-needle aspiration biopsy (USeFNAB), which employs a needle that flexurally oscillates at an ultrasonic frequency of ∼32 kHz. The needle motion contributes to increased tissue collection while preserving cells and tissue constructs for pathological assessment. Previously, USeFNAB has been investigated only in ex vivo animal tissue. The present study was aimed at determining the feasibility of using USeFNAB in human epithelial and lymphoid tissue. METHODS: Needle biopsy samples were acquired using FNAB, CNB and USeFNAB on ex vivo human tonsils (N = 10). The tissue yield and quality were quantified by weight measurement and blinded pathologists' assessments. The biopsy methods were then compared. RESULTS: The results revealed sample mass increases of, on average, 2.3- and 5.4-fold with USeFNAB compared with the state-of-the-art FNAB and CNB, respectively. The quality of tissue fragments collected by USeFNAB was equivalent to that collected by the state-of-the-art methods in terms of morphology and immunohistochemical stainings made from cell blocks as judged by pathologists. CONCLUSION: Our study indicates that USeFNAB is a promising method that could improve tissue yield to ensure sufficient material for ancillary histochemical and molecular studies for diagnostic pathology, thereby potentially increasing diagnostic accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".