Yu Tong, Zhao Xiaoyan, Leader Joseph K, Wang Jing, Meng Xin, Herman James, Wilson David, Pu Jiantao
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Cancers (Basel). 2024 Sep 26;16(19):3274. doi: 10.3390/cancers16193274.
This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy.
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.
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.
Arteries within 5.0 mm from the nodule surface emerged as a potential biomarker for effectively discriminating between malignant and benign nodules.
本研究旨在探讨肺结节周围动静脉与结节恶性程度之间的关联。
本研究使用了来自低剂量CT肺癌筛查项目的146名受试者的数据集。采用人工智能算法自动分割并量化结节及其周围的大血管。将大血管区分为动脉和静脉。对距结节表面不同距离处的动脉和静脉的血管分支数量、体积和迂曲度进行量化。进行单因素和多因素逻辑回归（LR）分析，特别关注直径为8至20毫米的结节。基于k折交叉验证法，使用ROC-AUC评估性能。在几种机器学习模型中评估平均特征重要性。
使用大血管特征的LR模型对所有结节的AUC为0.78（95%CI：0.71-0.86），对8-20毫米的结节的AUC为0.67（95%CI：0.54-0.80）。包括大血管特征、人口统计学特征和CT衍生结节特征的模型对所有结节的AUC为0.91（95%CI：0.87-0.96），对8-20毫米的结节的AUC为0.82（95%CI：0.71-0.92）。在特征重要性方面，距结节表面5.0毫米内的动脉在大血管特征中排名最高，即使纳入人口统计学特征和CT衍生结节特征后仍具有显著性。
距结节表面5.0毫米内的动脉成为有效区分恶性和良性结节的潜在生物标志物。
