Luo Qinyue, Li Hanting, Liu Xiaoqing, Zheng Yuting, Guo Tingting, Fan Jun, Wang Na, Han Xiaoyu, Shi Heshui
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
Transl Lung Cancer Res. 2024 Dec 31;13(12):3352-3363. doi: 10.21037/tlcr-24-459. Epub 2024 Dec 27.
Visceral pleural invasion (VPI) is associated with a poor outcome in early-stage non-small cell lung cancer (NSCLC). Preoperative prediction of VPI could have an impact on surgical planning. The aim of this study was to establish a nomogram model based on computed tomography (CT) features to predict VPI in early-stage NSCLC.
This study is a retrospective review of patients enrolled with surgically pathologically confirmed NSCLC between December 2019 and June 2022. Patients were divided into training and testing cohorts at a ratio of 7:3. Clinicopathologic and radiologic characteristics such as types of tumor pleura relationships (types I-V) were recorded. Multivariable logistic regression analysis was used to identify independent risk factors for VPI, and the optimized variables were used to build a nomogram model. Model performance was evaluated with receiver operating characteristic (ROC) curves and calibration curves. The clinical utility of the nomogram was determined using decision curve analysis (DCA).
Of the 766 patients [56.9% female patients; median age, 59 years; interquartile range (IQR): 53, 66] with early-stage NSCLC, VPI was confirmed in 250 patients (32.6%). There were 536 individuals in the training cohort (172 with VPI and 364 without VPI), and 230 individuals in the testing cohort (78 with VPI and 152 without VPI). The preoperative CT features related to VPI were tumor pleura relationship of type I and type III, solid, maximum diameter of tumor, lobulation, and lymphadenopathy. There was good discriminative power in the nomogram that included these six features. The training and testing cohorts' areas under the ROC curve (AUCs) were 0.815 and 0.825, respectively, with well-fitting calibration curves. DCA demonstrated that the nomogram was clinically useful.
The nomogram established with the identified CT features has the potential to assist with the prediction of VPI preoperatively in early-stage NSCLC and facilitate the selection of a rational treatment strategy.
脏层胸膜侵犯（VPI）与早期非小细胞肺癌（NSCLC）的不良预后相关。术前预测VPI可能会影响手术规划。本研究的目的是基于计算机断层扫描（CT）特征建立一个列线图模型，以预测早期NSCLC中的VPI。
本研究是一项对2019年12月至2022年6月期间手术病理确诊为NSCLC的患者的回顾性研究。患者按7:3的比例分为训练组和测试组。记录肿瘤胸膜关系类型（I-V型）等临床病理和放射学特征。采用多变量逻辑回归分析确定VPI的独立危险因素，并使用优化后的变量建立列线图模型。使用受试者操作特征（ROC）曲线和校准曲线评估模型性能。使用决策曲线分析（DCA）确定列线图的临床实用性。
在766例早期NSCLC患者中[女性患者占56.9%；中位年龄59岁；四分位间距（IQR）：53，66]，250例（32.6%）确诊为VPI。训练组有536例个体（172例有VPI，364例无VPI），测试组有230例个体（78例有VPI，152例无VPI）。与VPI相关的术前CT特征为I型和III型肿瘤胸膜关系、实性、肿瘤最大直径、分叶和淋巴结病。包含这六个特征的列线图具有良好的判别能力。训练组和测试组的ROC曲线下面积（AUC）分别为0.815和0.825，校准曲线拟合良好。DCA表明列线图具有临床实用性。
利用所确定的CT特征建立的列线图有潜力在术前辅助预测早期NSCLC中的VPI，并有助于选择合理的治疗策略。
