Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China.
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Eur Radiol. 2023 Jun;33(6):3931-3940. doi: 10.1007/s00330-022-09379-x. Epub 2023 Jan 4.
This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.
The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results.
The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%).
The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC.
• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.
本研究旨在基于高分辨率 CT（HRCT）特征预测 IA 期肺浸润性腺癌（IAC）的高级别模式（HGP）。
回顾性分析了 457 例（来自两个中心）经病理证实的 IA 期 IAC 患者（共 459 处病变）的临床、病理和 HRCT 影像学数据。根据病理结果中是否存在 HGP（微乳头状和实体状），将 459 处病变分为高级别模式（HGP）（n=101）和非高级别模式（n-HGP）（n=358）组。临床和病理数据包括年龄、性别、吸烟史、肿瘤分期、病理类型以及是否存在肿瘤气腔播散（STAS）。CT 特征包括病变位置、大小、密度、形状、分叶、空泡、空气支气管征和胸膜凹陷。采用单变量和多变量逻辑回归分析筛选 HGP 的独立预测因子。根据多变量分析结果构建临床、CT 和临床-CT 模型。
多变量分析提示 HGP 的独立预测因子包括肿瘤大小（p=0.001；OR=1.090，95%CI 1.035-1.148）、密度（p<0.001；OR=9.454，95%CI 4.911-18.199）和分叶（p=0.002；OR=2.722，95%CI 1.438-5.154）。用于预测 HGP 的临床、CT 和临床-CT 模型的 AUC 值分别为 0.641（95%CI 0.583-0.699）（敏感度=69.3%，特异度=79.2%）、0.851（95%CI 0.806-0.896）（敏感度=79.2%，特异度=79.6%）和 0.852（95%CI 0.808-0.896）（敏感度=74.3%，特异度=85.8%）。
基于 HRCT 特征的逻辑回归模型对 IA 期 IAC 的高级别模式具有良好的诊断性能。
