文章：

肺结节恶性血管生物标志物：动脉与静脉对比研究

Vascular Biomarkers for Pulmonary Nodule Malignancy: Arteries vs. Veins

原文发布日期：26 September 2024

DOI: 10.3390/cancers16193274

类型: Article

开放获取: 是

英文摘要：

Objective: This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy. Methods: A dataset of 146 subjects from a LDCT lung cancer screening program was used in this study. AI algorithms were used to automatically segment and quantify nodules and their surrounding macro-vasculature. The macro-vasculature was differentiated into arteries and veins. Vessel branch count, volume, and tortuosity were quantified for arteries and veins at different distances from the nodule surface. Univariate and multivariate logistic regression (LR) analyses were performed, with a special emphasis on the nodules with diameters ranging from 8 to 20 mm. ROC-AUC was used to assess the performance based on the k-fold cross-validation method. Average feature importance was evaluated in several machine learning models. Results: The LR models using macro-vasculature features achieved an AUC of 0.78 (95% CI: 0.71–0.86) for all nodules and an AUC of 0.67 (95% CI: 0.54–0.80) for nodules between 8–20 mm. Models including macro-vasculature features, demographics, and CT-derived nodule features yielded an AUC of 0.91 (95% CI: 0.87–0.96) for all nodules and an AUC of 0.82 (95% CI: 0.71–0.92) for nodules between 8–20 mm. In terms of feature importance, arteries within 5.0 mm from the nodule surface were the highest-ranked among macro-vasculature features and retained their significance even with the inclusion of demographics and CT-derived nodule features. Conclusions: Arteries within 5.0 mm from the nodule surface emerged as a potential biomarker for effectively discriminating between malignant and benign nodules.

摘要翻译： 

目的：本研究旨在探讨肺结节周围动静脉与其恶性程度之间的关联。方法：本研究采用来自低剂量CT肺癌筛查项目的146例受试者数据集。通过人工智能算法自动分割并量化结节及其周围宏观血管结构。宏观血管被区分为动脉和静脉。针对距离结节表面不同范围内的动静脉，分别量化其血管分支数量、体积及迂曲度。研究采用单变量及多变量逻辑回归分析，特别关注直径在8至20毫米范围内的结节。通过k折交叉验证法，使用ROC-AUC评估模型性能，并在多种机器学习模型中评估特征平均重要性。结果：使用宏观血管特征的逻辑回归模型对所有结节的预测AUC达0.78（95% CI：0.71–0.86），对8–20毫米结节的AUC为0.67（95% CI：0.54–0.80）。结合宏观血管特征、人口统计学数据和CT衍生结节特征的模型对所有结节的AUC提升至0.91（95% CI：0.87–0.96），对8–20毫米结节的AUC达0.82（95% CI：0.71–0.92）。在特征重要性评估中，距离结节表面5.0毫米范围内的动脉在宏观血管特征中排名最高，即使在纳入人口统计学和CT衍生结节特征后仍保持显著意义。结论：距离结节表面5.0毫米范围内的动脉可作为有效区分恶性与良性结节的潜在生物标志物。

原文链接：

Vascular Biomarkers for Pulmonary Nodule Malignancy: Arteries vs. Veins