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Analysis of volatile organic compounds in biological samples of colorectal cancer patients using electronic

Nada E Ahmed1,2, Mohamed S Mshaly2, Khaled M Madbouly3

  • 1Medical Biophysics Department, Medical Research Institute, Alexandria University, 165 El-Horreya Avenue, Alexandria, 5433005, Egypt.

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|December 25, 2025
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Summary
This summary is machine-generated.

An electronic nose (eNose) detects colorectal cancer (CRC) by analyzing volatile organic compounds (VOCs) in blood, urine, and stool. Machine learning models, particularly gradient boosting, show high accuracy for non-invasive CRC diagnosis.

Keywords:
Cancer screeningColorectal cancer (CRC)Electronic nose (eNose)Headspace analysisMachine learning (ML)Volatile organic compounds (VOCs)

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Area of Science:

  • Biomedical Engineering
  • Oncology
  • Computational Biology

Background:

  • Colorectal cancer (CRC) presents a significant global health challenge with high mortality due to late detection.
  • Early detection and prevention through screening, including adenoma removal, are crucial for improving CRC outcomes.
  • Volatile organic compounds (VOCs) in biological samples offer potential biomarkers for non-invasive disease diagnosis.

Purpose of the Study:

  • To investigate the efficacy of an electronic nose (eNose) coupled with machine learning (ML) for diagnosing colorectal cancer (CRC) by analyzing VOCs.
  • To compare the performance of various ML algorithms (PCA, LR, KNN, SVM, GB) in detecting CRC across different biological matrices (blood, urine, stool).
  • To assess the potential of eNose-based VOC analysis as an accessible, non-invasive, and affordable tool for CRC screening and early diagnosis.

Main Methods:

  • A prospective study involving 100 participants (50 stage III CRC patients, 50 healthy controls) recruited between 2024-2025.
  • Analysis of VOCs in blood, urine, and stool samples using an eNose technique.
  • Application of unsupervised (PCA) and supervised (LR, KNN, SVM, GB) ML algorithms to eNose data for CRC detection.

Main Results:

  • CRC patients exhibited distinct VOC patterns across all biological matrices compared to healthy controls.
  • Blood and stool samples provided the most informative VOC signals due to superior signal-to-noise ratios.
  • Ensemble and proximity-based ML models, specifically Gradient Boosting (GB) and K-Nearest Neighbor (KNN), outperformed Logistic Regression (LR) in diagnostic accuracy.
  • Gaussian augmentation enhanced model performance and generalizability for screening applications, mitigating limitations of focusing on stage III CRC.

Conclusions:

  • eNose technology combined with ML offers a promising, innovative, non-invasive, and cost-effective approach for CRC detection.
  • The developed eNose-based ML system demonstrates high sensitivity and specificity, supporting widespread early diagnosis and potentially improving patient outcomes.
  • This technology holds potential as a globally accessible tool to augment current CRC screening strategies.