CT影像组学对囊腔型肺癌的诊断价值

doi:10.6040/j.issn.1671-7554.0.2023.0748

摘要/Abstract

摘要： 目的建立一个基于临床特征和术前计算机断层扫描(CT)影像组学的预测模型辅助诊断囊腔型肺癌。方法回顾性分析2020年1月至2021年12月在青岛大学附属医院及济宁医学院附属医院接受手术治疗的患者,纳入病变中含有囊腔的患者296例,并分为囊腔型肺癌和良性病变两组。从术前CT图像上提取病灶的影像组学特征,并综合分析临床病理特征。将上述特征进行筛选后以随机森林算法构建诊断模型,其中来自青岛大学附属医院的214例患者进行训练和内部验证(D1)。其余82例来自济宁医学院附属医院的患者作为独立的外部验证队列(D2)。通过受试者工作特征曲线(ROC)、校准曲线及决策曲线分析(DCA)对模型的分类能力进行评估。结果囊腔型肺癌组患者年龄高于良性病变组(60.12±9.95 vs 50.86 ±13.66, P<0.001),其余临床特征差异无统计学意义。影像组学特征经筛选后获得形状平坦度、稳健平均绝对偏差、差异方差等5种影像组学纹理特征。临床与影像组学特征融合后构建随机森林模型在D1(AUC 0.97)和D2(AUC 0.74)上均显示出较好的诊断效能,在D1中的准确性、敏感性与特异性分别为0.92、0.85和0.99。结论构建并外部验证了一个实用的诊断模型,可以比较准确地区分囊腔型肺癌和囊腔样良性病变。这种创新的方法可作为囊腔型肺癌的一种新的无创诊断工具。

关键词: 囊腔型肺癌, 影像组学, 外部验证, 计算机断层扫描

Abstract: Objective To develop a prediction model based on clinical features and preoperative computed tomography(CT), which would serve as a non-invasive method for diagnosing lung cancers associated with cystic airspaces(LCCA). Methods Clinical data of patients undergoing surgery during Jan. 2020 and Dec. 2021 were retrospectively reviewed. A total of 296 patients were enrolled and divided into LCCA group and benign group. Radiomics features were extracted from lesions on preoperative CT images, and clinicopathological characteristics were carefully analyzed. The aforementioned features were used to construct a diagnostic model with random forest algorithm following feature reduction. The 214 patients from the Affiliated Hospital of Qingdao University underwent training and internal verification(D1). The remaining 82 patients from the Affiliated Hospital of Jining Medical College served as an independent external validation cohort(D2). The classification ability of the model was evaluated with receiver operating characteristic(ROC)curve, calibration curve and decision curve analysis(DCA). Results There were no other significant differences between the two groups in clinical features except that the LCCA group averaged older than the benign group(60.12±9.95 vs. 50.86±13.66, P<0.001). After radiomic features were screened, the following features were obtained, including shape flatness, robust mean absolute deviation and difference variance. The model demonstrated superior performance in discriminating LCCA in both D1(AUC 0.97)and D2(AUC 0.74), with accuracy, sensitivity and specificity on D1 being 0.92, 0.85 and 0.99, respectively. Conclusion We have developed and externally validated a practical model that can accurately differentiate LCCA from benign lesions. This innovative approach may serve as a new non-invasive diagnostic tool for LCCA.

Key words: Lung cancer associated with cystic airspaces, Radiomics, External validation, Computed tomography

中图分类号:

R734.2

艾江山,高会江,艾仕文,李恒艳,石国栋,魏煜程. CT影像组学对囊腔型肺癌的诊断价值[J]. 山东大学学报 (医学版), 2023, 61(12): 70-77.

AI Jiangshan, GAO Huijiang, AI Shiwen, LI Hengyan, SHI Guodong, WEI Yucheng. Diagnostic value of CT radiomics in lung cancer associated with cystic airspaces[J]. Journal of Shandong University (Health Sciences), 2023, 61(12): 70-77.

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