Department of Otorhinolaryngology-Head and Neck Surgery, Flinders Medical Centre, Bedford Park, Australia.
Discipline of Surgery, College of Medicine and Public Health, Flinders University, Bedford Park, Australia.
Br J Cancer. 2020 Dec;123(12):1775-1781. doi: 10.1038/s41416-020-01051-9. Epub 2020 Sep 9.
Improving the ability to identify early-stage head and neck squamous cell carcinoma (HNSCC) can improve treatment outcomes and patient morbidity. We sought to determine the diagnostic accuracy of breath analysis as a non-invasive test for detecting HNSCC.
Standardised breath samples were collected from 181 patients suspected of HNSCC prior to any treatment. A selected ion flow-tube mass spectrometer was used to analyse breath for volatile organic compounds. Diagnosis was confirmed by histopathology. A binomial logistic regression model was used to differentiate breath profiles between cancer and control (benign disease) patients based on mass spectrometry derived variables.
In all, 66% of participants had early-stage primary tumours (T1 and T2) and 58% had regional node metastasis. The optimised logistic regression model using three variables had a sensitivity and specificity of 80% and 86%, respectively, with an AUC for ROC curve of 0.821 (95%CI 0.625-1.0) in the testing cohort.
Breath analysis for non-invasive diagnosis of HNSCC appears to be practical and accurate. Future studies should be conducted in a primary care setting to determine the applicability of breath analysis for early identification of HNSCC.
提高识别头颈部鳞状细胞癌(HNSCC)早期阶段的能力可以改善治疗效果和患者的发病率。我们旨在确定呼吸分析作为一种非侵入性检测方法用于检测 HNSCC 的诊断准确性。
在任何治疗之前,从 181 例疑似 HNSCC 的患者中采集标准化的呼吸样本。使用选择离子流管质谱仪分析呼吸中的挥发性有机化合物。通过组织病理学诊断确认。基于质谱衍生变量,使用二项逻辑回归模型对癌症和对照组(良性疾病)患者的呼吸谱进行区分。
在所有参与者中,66%的人患有早期原发性肿瘤(T1 和 T2),58%的人有区域淋巴结转移。在测试队列中,使用三个变量的优化逻辑回归模型的灵敏度和特异性分别为 80%和 86%,ROC 曲线的 AUC 为 0.821(95%CI 0.625-1.0)。
呼吸分析用于非侵入性诊断 HNSCC 似乎是可行且准确的。未来的研究应在初级保健环境中进行,以确定呼吸分析在早期识别 HNSCC 中的适用性。