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Sniffing Out Lung Cancer: Biomimetic Breath Analysis via a Deep Eutectic Solvent-Driven Colorimetric Sensor Array.

Shiva Pesaran1, Zahra Shojaeifard1, Javad Tashkhourian1

  • 1Chemistry Department, Shiraz University, Shiraz 71454, Iran.

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|December 8, 2025
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Summary

A new paper-based sensor array detects lung cancer biomarkers in breath using deep eutectic solvents. This noninvasive tool shows high accuracy in identifying cancer patients, offering a promising screening method.

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Area of Science:

  • Analytical Chemistry
  • Biomedical Engineering
  • Environmental Science

Background:

  • Lung cancer is a leading cause of death globally, with diagnosis often delayed due to a lack of noninvasive early detection methods.
  • Current diagnostic tools for lung cancer can be invasive, costly, or lack accessibility, hindering early intervention and patient outcomes.

Purpose of the Study:

  • To develop and validate a novel, cost-effective, and noninvasive paper-based sensor array for detecting lung cancer-specific volatile organic compounds (VOCs) in exhaled breath.
  • To assess the sensor array's accuracy in distinguishing lung cancer patients from healthy individuals and those with other respiratory diseases.

Main Methods:

  • A paper-based colorimetric sensor array utilizing deep eutectic solvents (DES) and pH indicators was designed to capture and detect lung cancer VOCs.
  • Breath samples from 91 participants (lung cancer patients, noncancer respiratory disease patients, and healthy controls) were analyzed using a portable collection system.
  • Statistical analyses, including linear discriminant analysis (LDA), hierarchical cluster analysis (HCA), and ROC curves, were employed to evaluate sensor performance.

Main Results:

  • The sensor array achieved 93% accuracy in discriminating lung cancer patients from noncancerous individuals, with 98% sensitivity and specificity.
  • The system demonstrated 87% accuracy in differentiating lung cancer from other respiratory conditions, addressing a significant clinical need.
  • The use of hydrophobic DES enhanced sensor stability and selectivity by minimizing moisture interference and amplifying VOC interactions.

Conclusions:

  • The developed paper-based sensor array presents a highly accurate, rapid, and noninvasive method for early lung cancer detection through breath analysis.
  • This cost-effective technology holds significant potential for widespread application in large-scale lung cancer screening programs.
  • The sensor array offers a transformative alternative to conventional diagnostic approaches, potentially improving patient survival rates through earlier diagnosis.