{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(10)"],"submitter":["Alshammari AH"],"pubmed_abstract":["Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In <i>C. elegans</i>, chemotaxis assays on urine samples achieved sensitivities of 87-96% and specificities of 90-95% in case-control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70-76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94-95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82-100% classification accuracy within 250 ms in pilot studies (n ≈ 20-30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics."],"journal":["Biomedicines"],"pagination":["2409"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12561682"],"repository":["biostudies-literature"],"pubmed_title":["Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics."],"pmcid":["PMC12561682"],"pubmed_authors":["Mahdi MF","di Luccio E","Alshammari AH","Morishita M","Hatakeyama H","Hirotsu T"],"additional_accession":[]},"is_claimable":false,"name":"Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.","description":"Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In <i>C. elegans</i>, chemotaxis assays on urine samples achieved sensitivities of 87-96% and specificities of 90-95% in case-control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70-76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94-95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82-100% classification accuracy within 250 ms in pilot studies (n ≈ 20-30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-14T03:19:37.988Z","creation":"2026-05-14T03:12:29.735Z"},"accession":"S-EPMC12561682","cross_references":{"pubmed":["41153692"],"doi":["10.3390/biomedicines13102409"]}}