Li Shuhua, Li Yang, Meng Ying, Huang Jingcheng, Gu Yihong, Song Yan, Zhang Shuni, Zhang Zhiya, Zhao Weiming, Xie Zongyu
Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu233004, China.
Department of Medical Imaging Diagnostics, Bengbu Medical University, Bengbu 233030, China.
Curr Med Imaging. 2025;21:e15734056383032. doi: 10.2174/0115734056383032250320041531.
This study seeks to assess vasculogenic mimicry (VM) occurrence in lung adenocarcinoma (LUAD) by delineating intratumoral and peritumoral characteristics using preoperative CT-based radiomics and a nomogram for enhanced precision.
Our retrospective analysis enrolled 150 LUAD patients, ascertained their VM status, and stratified them randomly into development (n=105) and validation cohorts. We extracted radiomics features from intra- and peritumoral zones, delineating 3, 5, and 7mm expansions on thin-section chest CT images. We formulated logistic models encompassing a clinical model (CM), intratumoral radiomics model (TRM), peritumoral radiomics models at 3, 5, and 7 mm (PRMs), and a composite model integrating both intra- and peritumoral zones (CRM). A radiomics nomogram model (RNM) was devised, amalgamating the Rad-scores from intra- and peritumoral regions with clinical-radiological traits to forecast VM. The models' efficacy was gauged via the receiver operating characteristic (ROC) curve analysis, calibration assessment, and decision curve analysis (DCA).
The CRM outperformed its counterparts, the TRM, PRM_3mm, PRM_5mm, and PRM_7mm models, with AUCs reaching 0.859 and 0.860 in the development and validation cohorts. Within the CM, tumor size and spiculation emerged as significant predictive covariates. The RNM, integrating independent predictors with the CRM-Rad-score, demonstrated clinical utility, achieving AUCs of 0.903 and 0.931 in the respective cohorts.
Our findings underscore the potential of CT-based radiomics characteristics derived from intratumoral and peritumoral regions to assess VM presence in LUAD patients. Combining radiomics signatures with clinicoradiological parameters within a nomogram framework significantly enhances predictive accuracy.
本研究旨在通过术前基于CT的影像组学描绘肿瘤内和肿瘤周围特征并构建列线图以提高精度，从而评估肺腺癌（LUAD）中血管生成拟态（VM）的发生情况。
我们的回顾性分析纳入了150例LUAD患者，确定其VM状态，并将他们随机分为训练组（n = 105）和验证组。我们从肿瘤内和肿瘤周围区域提取影像组学特征，在胸部薄层CT图像上划定3mm、5mm和7mm的扩展区域。我们构建了逻辑模型，包括临床模型（CM）、肿瘤内影像组学模型（TRM）、3mm、5mm和7mm的肿瘤周围影像组学模型（PRM）以及整合肿瘤内和肿瘤周围区域的复合模型（CRM）。设计了一个影像组学列线图模型（RNM），将肿瘤内和肿瘤周围区域的Rad评分与临床放射学特征相结合以预测VM。通过受试者操作特征（ROC）曲线分析、校准评估和决策曲线分析（DCA）来评估模型的效能。
CRM优于其他模型，即TRM、PRM_3mm、PRM_5mm和PRM_7mm模型，在训练组和验证组中的AUC分别达到0.859和0.860。在CM中，肿瘤大小和毛刺征是显著的预测协变量。RNM将独立预测因子与CRM-Rad评分相结合，显示出临床实用性，在各自队列中的AUC分别为0.903和0.931。
我们的研究结果强调了从肿瘤内和肿瘤周围区域获得的基于CT的影像组学特征在评估LUAD患者VM存在情况方面的潜力。在列线图框架内将影像组学特征与临床放射学参数相结合可显著提高预测准确性。
