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Emerging Electronic Nose Design for Breath-Based Cancer Diagnostics: Advances in Machine Learning Approaches and

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Summary

Early cancer detection is crucial for survival. This review explores advanced electronic nose (E-nose) technology using metal oxide sensors and machine learning for non-invasive breath analysis, aiming for accessible, multi-cancer diagnostics.

Keywords:
breath analysiscancer biomarkerschemiresistive sensorsearly cancer detectionelectronic nose (E-nose)machine learning (ML)metal oxide sensors (MOX)non-invasive diagnosticspattern recognitionvolatile organic compounds (VOCs)

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Nanotechnology

Background:

  • Early cancer detection significantly improves treatment outcomes and survival rates.
  • Current cancer screening methods are often invasive, costly, and rely on single biomarkers, limiting global accessibility.
  • Breath analysis using electronic noses (E-noses) presents a non-invasive, cost-effective alternative for detecting multiple cancer biomarkers.

Purpose of the Study:

  • To critically review advancements in metal oxide (MOX)-based E-nose systems for cancer detection.
  • To analyze machine learning (ML)-assisted data analysis pipelines for MOX E-noses.
  • To integrate recent breakthroughs in hybrid sensing, hardware drift mitigation, and clinical translation for multicancer diagnostics.

Main Methods:

  • Systematic analysis of ML-assisted MOX E-nose working principles, including sensor array response acquisition, feature extraction, and classification of volatile organic compounds (VOCs).
  • Evaluation of challenges such as sensor drift, cross-sensitivity, and real-world variability.
  • Integration of recent (2025-2026) innovations in hybrid sensing modalities and hardware-level drift mitigation.

Main Results:

  • Highlights the transition from individual sensors to integrated multisensor array chip (MSAC) architectures.
  • Discusses the systematic analysis of cancer-related VOCs and ML-driven data interpretation.
  • Identifies key barriers and recent breakthroughs in clinical translation and hardware improvements.

Conclusions:

  • ML-assisted MOX E-noses offer a promising platform for reliable, scalable, real-time multicancer diagnostics.
  • Bridging material innovations with system-level performance and deployment challenges is crucial for E-nose technology development.
  • This review provides a framework for advancing non-invasive cancer detection and reducing diagnostic inequality.