biostudies-literatureUnknown13(10)Alshammari AHOrganismal 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.Biomedicines2409https://www.ebi.ac.uk/biostudies/studies/S-EPMC12561682biostudies-literatureBeyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.PMC12561682Mahdi MFdi Luccio EAlshammari AHMorishita MHatakeyama HHirotsu TfalseBeyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.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.2025-01-01T00:00:00Z2025 Sep2026-05-14T03:19:37.988Z2026-05-14T03:12:29.735ZS-EPMC125616824115369210.3390/biomedicines13102409