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Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.

Aya Hasan Alshammari1, Monther F Mahdi2, Takaaki Hirotsu1

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Organismal biosensing uses living systems to detect cancer-related volatile organic compounds (VOCs). This review proposes a Dual-Pathway Framework to advance these low-cost, non-invasive diagnostics for clinical use.

Keywords:
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Area of Science:

  • Biotechnology
  • Cancer Diagnostics
  • Biosensors

Background:

  • Organismal biosensing utilizes the olfactory capabilities of living organisms to detect cancer biomarkers.
  • This approach offers a promising, low-cost, and non-invasive alternative to traditional cancer diagnostic methods.
  • Diverse biological platforms, including nematodes, canines, and insects, have shown feasibility in detecting cancer-associated volatile organic compounds (VOCs).

Purpose of the Study:

  • To review the current state of organismal biosensing for cancer detection.
  • To highlight the potential of various biological systems in identifying cancer-specific VOC signatures.
  • To propose a novel Dual-Pathway Framework for advancing organismal biosensing towards clinical application.

Main Methods:

  • Review of existing studies on organismal biosensing platforms (e.g., *C. elegans*, canines, insects) for cancer detection.
  • Analysis of reported sensitivities, specificities, and accuracies across different biosensing modalities.
  • Introduction of a proposed Dual-Pathway Framework integrating high-throughput screening and machine learning for advanced diagnostics.

Main Results:

  • Demonstrated feasibility of organismal biosensing across diverse platforms with varying diagnostic performance (e.g., *C. elegans* 87-96% sensitivity, canines ~71% sensitivity, insects 82-100% accuracy).
  • Validation that organismal behavior and neural activity encode cancer-related VOC signatures.
  • Identification of limitations including small cohort sizes and methodological heterogeneity.

Conclusions:

  • Organismal biosensing platforms show significant potential for detecting cancer-related VOCs.
  • The proposed Dual-Pathway Framework aims to overcome current limitations by combining screening and machine learning.
  • This integrated approach could facilitate the translation of organismal biosensing into scalable, precision cancer diagnostics.