aalto1 untyped-item.component.html
A core needle biopsy combined with novel spectroscopic probe for in vivo tissue classification – A pilot study on piglets
Loading...
Access rights
openAccess
CC BY
CC BY
Creative Commons license
Except where otherwised noted, this item's license is described as openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
Biomedical Engineering Advances, Volume 10, pp. 1-6
Abstract
Tissue sampling is a primary goal of core needle biopsies (CNB), cancer therapy evaluation, and autoimmune disease assessment. Conventional guidance methods such as ultrasound and MRI suffer from periprocedural tissue‐type insensitivity in complex biopsy targets, motion sensitivity, imaging artifacts and high costs, which may limit their usefulness. Accurate tissue classification and needle guidance during CNB are equally important. Mistakes may lead to sample inadequacies, obscured results, incorrect sampling spots, and ultimately repeated biopsies. To address these challenges, this study investigates the feasibility of a smart CNB probe integrating real-time optical spectroscopy for enhanced tissue characterization during in vivo biopsy utilizing machine learning methods. Ten fabricated probes were tested in vivo on porcine fat, liver, and kidney tissues, demonstrating potential for improving biopsy accuracy. Acquired spectral data enabled effective tissue differentiation, as indicated by the best-performing classification models. LDA classifier with MRMR feature selection reached sensitivity of 87.3 % in classification between liver and fat tissues, where SVM with linear kernel and PCA reached 86.4 % sensitivity in kidney vs fat. These findings suggest that integrating optical spectroscopy into CNB procedures may enhance diagnostic accuracy while mitigating procedural risks.
Description
Keywords
Other note
Citation
Surazynski, L, Järvinen, J, Ilvesmäki, M, Mäkinen, M, Nieminen, H J, Nieminen, M T & Myllylä, T 2025, 'A core needle biopsy combined with novel spectroscopic probe for in vivo tissue classification – A pilot study on piglets', Biomedical Engineering Advances, vol. 10, 100191, pp. 1-6. https://doi.org/10.1016/j.bea.2025.100191
