Ultrasound-enhanced fine-needle biopsy improves tissue yield in head and neck tumors ex vivo
Bibliographic record
Abstract
Current needle biopsy techniques, i.e., fine-needle aspiration biopsy (FNAB) and core needle biopsy (CNB), are widely utilized in cancer diagnostics but have certain shortcomings. The limited yield of diagnostic cellular material and the low sensitivity to detect various types of malignancies are well-known problems with FNAB. In contrast, CNB provides a histological sample but is typically more labor-intensive to obtain. Ultrasound-enhanced FNAB (USeFNAB) represents a novel approach that utilizes an ultrasonically oscillating fine-needle tip to enhance tissue yield. This study aims to assess the feasibility of employing USeFNAB in head and neck tumors in an ex vivo setting. Parotid gland tumors (PGT; N = 10) and neck lymph nodes of patients with head and neck cancer (HNC; N = 10) were resected and biopsied using three techniques: USeFNAB, FNAB, and CNB which served as a comparative method. The samples obtained were weighed and the yield and quality of the tissue fragments were evaluated by pathologists. Immunohistochemical staining was performed to determine whether USeFNAB had any impact on the staining characteristics of the sample. The findings showed that USeFNAB, at a 0.5 W power level, produced a tissue sample (mass) that was 1.9 times higher in the PGT group, and 4.6 times higher in the HNC group compared to FNAB. The quality of the sample obtained via USeFNAB was comparable to that of FNAB. USeFNAB did not alter the immunohistochemical staining characteristics. Overall, USeFNAB appears to be a promising tool for improving the tissue yield of fine-needle biopsy and enhancing 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 | 0.000 | 0.000 |
| 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".