Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review

Abstract

This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to comprehensively explore how these biomarkers can serve as early indicators of various cancers, enhancing diagnostic precision and reducing invasiveness. A total of 120 studies published between 2018 and 2023 were examined through systematic mapping and literature review methodologies, employing the PICOS (Population, Intervention, Comparison, Outcome, and Study design) methodology to guide the analysis. Of these studies, 65.83% were ranked in Q1 journals, illustrating the scientific rigor of the included research. Our review synthesizes both technical and clinical perspectives, evaluating sensor-based devices such as gas chromatography-mass spectrometry and selected ion flow tube-mass spectrometry with reported incidences of 30 and 8 studies, respectively. Key analytical techniques including Support Vector Machine, Principal Component Analysis, and Artificial Neural Networks were identified as the most prevalent, appearing in 22, 24, and 13 studies, respectively. While substantial improvements in detection accuracy and sensitivity are noted, significant challenges persist in sensor optimization, data integration, and adaptation into clinical settings. This comprehensive analysis bridges existing research gaps and lays a foundation for the development of non-invasive diagnostic devices. By refining detection technologies and advancing clinical applications, this work has the potential to transform cancer diagnostics, offering higher precision and reduced reliance on invasive procedures. Our aim is to provide a robust knowledge base for researchers at all experience levels, presenting insights on sensor capabilities, metrics, analytical methodologies, and the transformative impact of emerging electronic nose technologies in clinical practice.

Keywords: Principal Component Analysis; Support Vector Machine; biomarkers; cancer detection; components; eNose; machine learning techniques; medical devices; sensors; systematic review.

Figure A1

VOCs categories: aldehyde and alcohol.

Figure A2

VOCs categories: hydrocarbon and ketone.

Figure A3

VOCs categories: acid and aromatic.

Figure A4

VOCs categories: colorless gas, ester, terpene, benzene, chemical, nitrile, cysteine, styrene and sulfur.

Figure A5

Compounds categories: alkane.

Figure A6

VOCs categories: alkylbenzene, amino acid and alkene.

Figure A7

Compounds categories: organic.

Figure 1

Distribution of annual publications and by type of study.

Figure 2

Taxonomy of the Study provides a comprehensive overview of VOCs and their role in cancer detection. It further complements the taxonomic framework by highlighting the classification of VOCs according to cancer types, the biological samples analyzed (breath, urine, saliva, and blood), and the advanced technologies utilized for their detection, such as chemical sensors and semiconductor-based devices. This figure offers a clear perspective on the interplay between chemical biomarkers, diagnostic tools, and analytical technologies, emphasizing the significance of VOCs in early diagnosis and oncological research.

Figure 3

Public database is identified in technical and medical studies.

Figure 4

Categories of VOCs and terminology for their identification.

Figure 5

The chart displays a frequency matrix that connects cancer types (vertical axis) with chemical formulas (horizontal axis). Each cell represents the frequency of association between a specific cancer type and a chemical formula, indicated by the intensity of the blue color scale. Rows correspond to specific cancer types, while columns represent coded chemical formulas. This format helps identify patterns and notable relationships, such as formulas highly associated with certain cancer types or cancer types sharing multiple formulas.

Figure 6

The chart presents the frequency of VOCs identified in technical (T) and medical (M) studies related to cancer, organized by chemical type and occurrence frequency. The horizontal axis displays the total occurrences of each compound, while the vertical axis lists their names and chemical formulas. The compounds are classified into specific chemical categories, visually distinguished by distinct colors assigned to each type, as explained in the legend.

Figure 7

Frequency of identified techniques in the studies and their occurrence in TS and MS.

Figure 8

Semiconductor and gas sensors.

Figure 9

Chemical, electrolyte, metal oxide, electrochemical, pressure, and humidity sensors.

Figure 10

Analytical, chemical method, analyte extraction technique.

Figure 11

Metrics identified in the technical and medical literature.