BACKGROUND Colorectal cancer (CRC) is the third most common cancer diagnosis in the Western world. AIM To evaluate exhaled volatile organic compounds (VOCs) as a non-invasive biomarker for the detection of CRC and precursor lesions using an electronic nose. METHODS In this multicentre study adult colonoscopy patients, without inflammatory bowel disease or (previous) malignancy, were invited for breath analysis. Two-thirds of the breath tests were randomly assigned to develop training models which were used to predict the diagnosis of the remaining patients (external validation). In the end, all data were used to develop final-disease models to further improve the discriminatory power of the algorithms. RESULTS Five hundred and eleven breath samples were collected. Sixty-four patients were excluded due to an inadequate breath test (n = 51), incomplete colonoscopy (n = 8) or colitis (n = 5). Classification was based on the most advanced lesion found; CRC (n = 70), advanced adenomas (AAs) (n = 117), non-advanced adenoma (n = 117), hyperplastic polyp (n = 15), normal colonoscopy (n = 125). Training models for CRC and AAs had an area under the curve (AUC) of 0.76 and 0.71 and blind validation resulted in an AUC of 0.74 and 0.61 respectively. Final models for CRC and AAs yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively. CONCLUSIONS This study suggests that exhaled VOCs could potentially serve as a non-invasive biomarker for the detection of CRC and AAs. Future studies including more patients could further improve the discriminatory potential of VOC analysis for the detection of (pre-)malignant colorectal lesions. (https://clinicaltrials.gov Identifier NCT03488537).

中文翻译：

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呼吸中的挥发性有机化合物可以用作检测晚期腺瘤和结直肠癌的非侵入性诊断生物标志物。

背景技术大肠癌（CRC）是西方世界中第三种最常见的癌症诊断。目的评估呼出的挥发性有机化合物（VOC），作为使用电子鼻检测CRC和前体病变的非侵入性生物标志物。方法在这项多中心研究中，邀请没有炎症性肠病或（先前）恶性肿瘤的成人结肠镜检查患者进行呼吸分析。呼吸测试的三分之二被随机分配用于开发训练模型，该模型用于预测其余患者的诊断（外部验证）。最后，所有数据都用于开发最终疾病模型，以进一步提高算法的区分能力。结果收集了511个呼吸样本。由于呼吸测试不足（n = 51），排除了64位患者，不完全结肠镜检查（n = 8）或结肠炎（n = 5）。分类是根据发现的最严重的病变进行的；CRC（n = 70），晚期腺瘤（AAs）（n = 117），非晚期腺瘤（n = 117），增生性息肉（n = 15），正常结肠镜检查（n = 125）。CRC和AA的训练模型的曲线下面积（AUC）为0.76和0.71，盲目验证的AUC分别为0.74和0.61。CRC和AA的最终模型得出的AUC分别为0.84（敏感性95％和特异性64％）和0.73（敏感性和特异性79％和59％）。结论这项研究表明，呼出的VOC可能作为检测CRC和AA的非侵入性生物标志物。包括更多患者在内的未来研究可能会进一步提高VOC分析在（大）恶性大肠病变检测中的鉴别潜力。