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

Early-stage detection of cancer is a key factor for successful treatment and improved survival. Yet, current screening approaches are often invasive, expensive, and limited to single biomarkers, which constrains their applicability at a global scale. Breath analysis offers a promising, non-invasive alternative capable of detecting multiple cancer biomarkers simultaneously, with potential to reduce diagnostic inequality across low- and high-income countries. Among available technologies, electronic noses (E-noses) have emerged as powerful platforms for detecting volatile organic compounds (VOCs) in exhaled breath. This review critically discusses advances in metal oxide (MOX)-based E-nose systems, highlighting the transition from individual sensors to integrated multisensor array chip (MSAC) architectures, advanced signal conditioning, and machine-learning (ML)-assisted data analysis pipelines. The working principle of ML-assisted MOX E-noses, including sensor array response acquisition, feature extraction, dimensionality reduction, and classification of complex VOC mixtures, is systematically analyzed, with particular attention to drift, cross-sensitivity, and real-world variability. Distinct from prior reviews, this work integrates a systematic analysis of cancer-related VOCs with recent breakthroughs (2025-2026) in hybrid sensing modalities, hardware-level drift mitigation, and clinical translation barriers. By bridging material-level innovations with system-level performance metrics and real-world deployment challenges, this work provides a critical framework for the development of reliable, scalable, and real-time E-nose technologies for multicancer diagnostics.