ArticleDiagnostics (Basel, Switzerland)2022
Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- Noninvasive Total Cholesterol Level Measurement Using an E-Nose System and Machine Learning on Exhaled Breath Samples.ACS sensors · 2024Article
- Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review.Journal of cancer research and clinical oncology · 2024Review
- Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.Sensors (Basel, Switzerland) · 2024Article
- Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management.Diagnostics (Basel, Switzerland) · 2023Article
- Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?Molecules (Basel, Switzerland) · 2023Article
- The role of lymph node dissection in the surgical treatment of endometrial cancer patients (retrospective analysis).Journal of cancer research and clinical oncology · 2023Article
- GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
15 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundGastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters).
methodsWe analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests.
resultsThe accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity.
conclusionsThe results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.
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