Lu Guangyu, Su Zhixia, Yu Xiaoping, He Yuhang, Sha Taining, Yan Kai, Guo Hong, Tao Yujian, Liao Liting, Zhang Yanyan, Lu Guotao, Gong Weijuan
Department of Health Management Center, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
Cancer Med. 2025 Jan;14(1):e70545. doi: 10.1002/cam4.70545.
Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored.
Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics.
A total of 267 participants were enrolled, including 210 with low-risk and 57 with moderate-risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate-risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611-0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974-1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data.
The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability.
Number ChiCTR2400081283.
成像技术的进步提高了肺结节的检测能力。然而，确定肺结节的恶性程度通常需要侵入性检查或反复接受辐射，这凸显了对更安全的非侵入性诊断方法的需求。分析呼出的挥发性有机化合物（VOCs）显示出一定前景，但其在评估肺结节恶性程度方面的有效性仍有待深入研究。
于2023年6月至2024年1月在扬州大学附属医院采用前瞻性研究设计，我们使用梅奥诊所模型评估肺结节的恶性程度，并收集呼出气体样本以及生活方式和健康检查数据。我们应用五种机器学习（ML）算法来开发预测模型，并使用曲线下面积（AUC）、敏感性、特异性和其他相关指标进行评估。
共纳入267名参与者，其中210名患有低风险肺结节，57名患有中度风险肺结节。单因素分析确定了11种与结节恶性程度相关的呼出VOCs，以及两个生活方式因素（吸烟指数和烟草烟雾吸入部位）和一个临床指标（结节直径）作为中度风险结节的独立预测因素。整合生活方式和健康数据的逻辑回归模型的AUC为0.91（95%CI：0.8611 - 0.9658），而纳入呼出VOCs的随机森林模型的AUC为0.99（95%CI：0.974 - 1.00）。校准曲线表明预测风险与观察到的风险之间具有高度一致性。决策曲线分析证实了这些模型相对于传统方法的净效益。开发了一种列线图，以帮助临床医生根据VOCs、生活方式和健康数据评估结节的恶性程度。
将ML算法与呼出生物标志物和临床数据相结合，为肺结节的非侵入性评估提供了一个强大的框架。这些模型为传统方法提供了更安全的替代方案，并可能加强肺结节的早期检测和管理。需要通过更大规模的多中心研究进行进一步验证，以确定其可推广性。
编号ChiCTR2400081283。
