Volatilomics for breast cancer diagnosis: A narrative review across biological matrices and clinical applications.

Breast cancer (BC) is the leading cause of cancer-related mortality among women worldwide. Early, non-invasive, and accurate detection remains critical to reducing mortality and improving clinical outcomes. Volatilomics, focused on the characterization of volatile organic compounds (VOCs) emitted by the human body, offers a promising approach for disease diagnosis. VOC-based testing has already been translated into routine clinical practice for a number of illnesses, yet such tests for BC remain unavailable. To accelerate translational progress within this field, a comprehensive synthesis of available evidence is urgently required. Existing reviews on volatilomics in BC are either constrained to single matrices (e.g., exhaled breath), focus on exogenous VOCs, provide only cursory coverage of BC within multi-cancer overviews, or fail to reflect recent advances. In this narrative review, we synthesize evidence from 46 studies published over the past two decades, including studies of exhaled breath (n = 24), urine (n = 10), exhaled breath condensate (EBC, n = 1), sweat (n = 2), tumor tissue (n = 1), cell lines (n = 6), and animal tumor models (n = 2). We review the application of volatilomics in diagnostic potential, pathological and molecular subtyping, clinical staging, risk prediction, and VOC generation mechanisms. Furthermore, we evaluate the impact of advanced analytical methods, such as the integration of volatilomics with other omics approaches to enhance diagnostic performance. In addition, we outline the major translational barriers restricting clinical transformation, followed by an in-depth discussion of prospective research directions including multi-matrix data integration, AI-assisted volatilomic modeling, and living olfactory biosensors. This review delivers an integrated framework to support subsequent mechanistic exploration and the transition from analytical feasibility toward clinical validation of VOC-based BC detection.