Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) is essential to improve patient prognosis and enable organ and function preservation treatments. The objective of this study is to assess the feasibility of using electrical bioimpedance (EBI) sensing technology to detect HNSCC tissue. A prospective study was carried out analyzing tissue from 46 patients undergoing surgery for HNSCC. The goal was the correct identification of pathologic tissue using a novel needle-based EBI sensing device and AI-based classifiers. Considering the data from the overall patient cohort, the system achieved accuracies between 0.67 and 0.93 when tested on tissues from the mucosa, skin, muscle, lymph node, and cartilage. Furthermore, when considering a patient-specific setting, the accuracy range increased to values between 0.82 and 0.95. This indicates that more reliable results may be achieved when considering a tissue-specific and patient-specific tissue assessment approach. Overall, this study shows that EBI sensing may be a reliable technology to distinguish pathologic from healthy tissue in the head and neck region. This observation supports the continuation of this research on the clinical use of EBI-based devices for early detection and margin assessment of HNSCC.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:13

Enthalten in:

Diagnostics (Basel, Switzerland) - 13(2023), 14 vom: 24. Juli

Sprache:

Englisch

Beteiligte Personen:

Carobbio, Andrea Luigi Camillo [VerfasserIn]
Cheng, Zhuoqi [VerfasserIn]
Gianiorio, Tomaso [VerfasserIn]
Missale, Francesco [VerfasserIn]
Africano, Stefano [VerfasserIn]
Ascoli, Alessandro [VerfasserIn]
Fragale, Marco [VerfasserIn]
Filauro, Marta [VerfasserIn]
Marchi, Filippo [VerfasserIn]
Guastini, Luca [VerfasserIn]
Mora, Francesco [VerfasserIn]
Parrinello, Giampiero [VerfasserIn]
Canevari, Frank Rikki Mauritz [VerfasserIn]
Peretti, Giorgio [VerfasserIn]
Mattos, Leonardo S [VerfasserIn]

Links:

Volltext

Themen:

Cancer detection
Classifier
Electrical bioimpedance
Head and neck cancer
Journal Article
Machine learning
Squamous cell carcinoma

Anmerkungen:

Date Revised 31.07.2023

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.3390/diagnostics13142453

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM360100678