Ultrasound-enhanced fine-needle biopsy improves tissue yield in head and neck tumors ex vivo

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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Publication Details

Journal
Scientific Reports
Published
2025-03-26
DOI
https://doi.org/10.1038/s41598-025-92610-8
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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Ultrasound-enhanced fine-needle biopsy improves tissue yield in head and neck tumors ex vivo

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article

Ultrasound-enhanced fine-needle biopsy improves tissue yield in head and neck tumors ex vivo

Jussi Tarkkanen, Heikki J. Nieminen, Timo S. Atula, Jaana Rautava, Katri L. S. Aro, Emanuele Perra, Jetta Kelppe, Nick Hayward, Kenneth P. H. Pritzker, Yohann Le Bourlout, Minna Rehell, Kristofer Nyman, Gösta Ehnholm, Jouni Rantanen
article en
1 citations

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.

Scientific ReportsVol. 15(1)
University of Helsinki (FI), University of Toronto (CA), Helsinki University Hospital (FI), Hospital District of Helsinki and Uusimaa (FI), Aalto University (FI)
Academy of Finland, Business Finland
Zero hunger
Openalex Percentile: Top 22%
Radiomics and Machine Learning in Medical Imaging
1.36
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