Zhu Jieling, Tian Fengjuan, Xie Zongyu, Shi Hengfeng, Yang Ting, Han Xiaoyu, Yan Cheng, Wei Fuquan, Wang Jian
Department of Radiology, Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University (Tongde Hospital of Zhejiang Province), Hangzhou, China.
Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8515-8528. doi: 10.21037/qims-24-1886. Epub 2025 Aug 15.
Lymphovascular invasion (LVI) is a high-risk pathological marker for the evaluation of metastasis and prognosis of non-small cell lung cancer (NSCLC). Preoperative computed tomography (CT) prediction of vascular invasion in NSCLC is essential for clinical identification of high-risk patients and development of treatment strategies. This study aimed to develop and validate a model for predicting LVI in NSCLC based on clinical and CT features.
A total of 2,830 patients with NSCLC confirmed by pathology and with complete clinical data were retrospectively enrolled. Among them, 2,663 were negative cases and 167 were positive cases. CT imaging and pathological data of these patients from Tongde Hospital of Zhejiang Province (center 1) and Anqing Municipal Hospital (center 2), from January 2015 to December 2023, were randomly divided into a training set and a validation set in a ratio of 7:3. Additionally, 275 patients from Taizhou Municipal Hospital (center 3) were assigned to the external validation set, including 242 negative cases and 33 positive cases. After screening for potential risk factors by univariate analysis, the selected risk factors were included in the multivariate binary logistic regression model to determine the independent risk factors of LVI in NSCLC to construct a prediction model and draw a nomogram, and the receiver operating characteristic (ROC) curve, calibration curve, and clinical impact curve (CIC) were used to evaluate the predictive power, discrimination, and clinical benefit of the model.
A total of 2,830 patients with NSCLC were included, including 1,190 (42.1%) males and 1,640 (57.9%) females, with a mean age of 61.15±10.83 years. Independent risk factors for LVI of NSCLC included the history of smoking, the history of diabetes mellitus, laboratory tumor indices, mixed ground-glass nodule (mGGN) consolidation/tumor ratio (CTR), and vacuole signs. The area under the curve (AUC), accuracy, sensitivity, and specificity for the training set were 0.836 [95% confidence interval (CI): 0.806-0.867], 65.2%, 92.1%, and 63.5%; those for the validation set were 0.803 (95% CI: 0.755-0.852), 71.6%, 82.7%, and 70.9%; and those for the external validation set were 0.845 (95% CI: 0.775-0.916), 70.9%, 87.8%, and 68.6%, respectively.
We developed and validated a model for predicting LVI in NSCLC based on clinical and CT image features. The model developed in this study has potential application value in predicting LVI in NSCLC. It provides a new, operable, and non-invasive technique for clinical identification of high-risk patients and may help clinical selection of appropriate treatment.
淋巴管浸润（LVI）是评估非小细胞肺癌（NSCLC）转移和预后的高危病理标志物。术前通过计算机断层扫描（CT）预测NSCLC中的血管浸润对于临床识别高危患者和制定治疗策略至关重要。本研究旨在基于临床和CT特征开发并验证一种预测NSCLC中LVI的模型。
回顾性纳入2830例经病理确诊且临床资料完整的NSCLC患者。其中，2663例为阴性病例，167例为阳性病例。将2015年1月至2023年12月期间来自浙江省同德医院（中心1）和安庆市立医院（中心2）的这些患者的CT影像和病理数据按7:3的比例随机分为训练集和验证集。此外，将来自台州市立医院（中心3）的275例患者纳入外部验证集，包括242例阴性病例和33例阳性病例。通过单因素分析筛选潜在危险因素后，将选定的危险因素纳入多因素二元逻辑回归模型，以确定NSCLC中LVI的独立危险因素，构建预测模型并绘制列线图，采用受试者操作特征（ROC）曲线、校准曲线和临床影响曲线（CIC）评估模型的预测能力、区分度和临床获益。
共纳入2830例NSCLC患者，其中男性1190例（42.1%），女性1640例（57.9%），平均年龄61.15±10.83岁。NSCLC中LVI的独立危险因素包括吸烟史、糖尿病史、实验室肿瘤指标、混合磨玻璃结节（mGGN）实性成分/肿瘤比率（CTR）和空泡征。训练集的曲线下面积（AUC）、准确率、灵敏度和特异度分别为0.836 [95%置信区间（CI）：0.806 - 0.867]、65.2%、92.1%和63.5%；验证集分别为0.803（95% CI：0.755 - 0.852）、71.6%、82.7%和70.9%；外部验证集分别为0.845（95% CI：0.775 - 0.916）、70.9%、87.8%和68.6%。
我们基于临床和CT图像特征开发并验证了一种预测NSCLC中LVI的模型。本研究开发的模型在预测NSCLC中LVI方面具有潜在应用价值。它为临床识别高危患者提供了一种新的、可操作的非侵入性技术，可能有助于临床选择合适的治疗方案。
