ArticleDiagnostics (Basel, Switzerland)2023
Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Novel bioparameters derived from bioimpedance measurements for accurate prediction of weight status in infant-juvenile individuals: A regression analysis.Journal of electrical bioimpedance · 2025Article
- Predictive classification and regression models for bioimpedance vector analysis: Insights from a southern Cuban cohort.Journal of electrical bioimpedance · 2025Article
- Assessment of a Bioimpedance-Based Method for the Diagnosis of Oral Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Integrating classification and regression learners with bioimpedance methods for estimating weight status in infants and juveniles from the southern Cuba region.BMC pediatrics · 2024Article
- The role of various physiological and bioelectrical parameters for estimating the weight status in infants and juveniles cohort from the Southern Cuba region: a machine learning study.BMC pediatrics · 2024Article
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Authors and funding
15 authors.
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Abstract
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.
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