{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T08:02:48Z","timestamp":1779696168361,"version":"3.53.1"},"reference-count":183,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration.<\/jats:p>","DOI":"10.3390\/life16050848","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:42:18Z","timestamp":1779691338000},"page":"848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7810-5011","authenticated-orcid":false,"given":"Mohamed A. A. 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