CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model.
CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model.
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DOI：
10.2147/ijgm.s368344
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发表时间：
2022
影响因子：
2.3
通讯作者：
中科院分区：
文献类型：
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目的探讨IA期肺浸润性粘液腺癌的CT表现特征，并建立预测模型。检查了2017年1月至2019年12月期间53例IA IMA患者的53个病灶，而141例浸润性非粘液性肺腺癌（INMA）患者的141个病灶作为对照病例。采用单因素分析比较两组患者的人口统计学特征和CT特征的差异，采用多因素Logistic回归分析确定孤立结节IMA的主要影响因素。根据这些因素的回归系数建立风险评分预测模型，并进行受试者工作特征（ROC）曲线分析以评价模型的预测性能。单因素分析显示，年龄、结节类型、结节最大直径、肿瘤肺界面、分叶征、毛刺征、支气管充气征或空泡征、异常血管改变在两组间差异有统计学意义（p < 0.05）。与INMA相比，IMA的毛刺相对较长且较软。多因素Logistic回归分析显示，结节类型、肿瘤肺界面不清、支气管充气征或空泡征、血管异常改变为主要影响因素。根据这些因素的回归系数建立了预测模型。ROC曲线下面积为0.882（p < 0.05）。与INMA相比，孤立性周围型IA结节性IMA在老年患者中更常见;他们在CT上更常出现不清楚的肿瘤肺界面和支气管充气征或空泡征;毛刺相对较长且较软;我们建立的基于结节类型、肿瘤肺界面、支气管充气征或空泡征以及异常血管改变的风险评分预测模型对孤立性结节性IMA具有良好的预测效果。
To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model. Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model. Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05). Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.