Clinical and translational gastroenterology · 2025
Detection Performance of Colorectal Cancer Through Exhaled Breath by Electronic Nose: A Case-Control Study.
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Wang Qiaoling, Tan Shiyan, Zheng Ruyi, Li Zhuohong, Chen Yuan, Huang Xiaopeng, Fang Yu
Аннотация
INTRODUCTION: Although colorectal cancer (CRC) screening has been incorporated into organized programs in many countries, a universally accepted noninvasive and efficient screening method remains unavailable. This study aimed to assess the diagnostic potential of volatile organic compounds in exhaled breath through electronic nose (eNose) for noninvasive CRC detection.
METHODS: The Cyranose320 sensor device was used to collect and analyze breath samples. Supervised machine learning was applied to evaluate the diagnostic performance of the eNose in CRC detection, using a randomly assigned training and validation set. Two-thirds of the breath samples were used to train models, which were then validated on the remaining patients (external validation). Three machine learning methods were applied for classification: random forest, extreme gradient boosting (XGBoost), and quadratic discriminant analysis.
RESULTS: A total of 105 patients with CRC and 101 healthy controls were included. After adjusting for baseline covariates (age, sex, smoking, and comorbidities), machine learning models based on volatile organic compound profiles could differentiate patients with CRC from healthy controls, achieving areas under the receiver operating characteristic curve (AUC) of at least 0.72 in both the training and validation sets. The final CRC classification models yielded AUCs of 0.93 for random forest, 0.88 for XGBoost, and 0.89 for quadratic discriminant analysis. Furthermore, eNose classified CRC by stage, with an AUC exceeding 0.70 for early and advanced disease.
DISCUSSION: Exhaled breath analysis using an eNose may serve as a promising noninvasive method for CRC detection. Further studies with larger populations are needed to confirm its clinical impact.
METHODS: The Cyranose320 sensor device was used to collect and analyze breath samples. Supervised machine learning was applied to evaluate the diagnostic performance of the eNose in CRC detection, using a randomly assigned training and validation set. Two-thirds of the breath samples were used to train models, which were then validated on the remaining patients (external validation). Three machine learning methods were applied for classification: random forest, extreme gradient boosting (XGBoost), and quadratic discriminant analysis.
RESULTS: A total of 105 patients with CRC and 101 healthy controls were included. After adjusting for baseline covariates (age, sex, smoking, and comorbidities), machine learning models based on volatile organic compound profiles could differentiate patients with CRC from healthy controls, achieving areas under the receiver operating characteristic curve (AUC) of at least 0.72 in both the training and validation sets. The final CRC classification models yielded AUCs of 0.93 for random forest, 0.88 for XGBoost, and 0.89 for quadratic discriminant analysis. Furthermore, eNose classified CRC by stage, with an AUC exceeding 0.70 for early and advanced disease.
DISCUSSION: Exhaled breath analysis using an eNose may serve as a promising noninvasive method for CRC detection. Further studies with larger populations are needed to confirm its clinical impact.