影像组学对磨玻璃结节型肺腺癌病理亚型的预测效能  

Subtype discrimination of lung adenocarcinoma manifesting as ground glass nodule based on radiomics

作　　者：范丽[1] 方梦捷[2] 董迪[2] 涂文婷 望云[1] 李琼[1] 萧毅[1] 田捷[2] 刘士远[1] Fan Li;Fang Mengfie;Dong Di;Tu Wenting;Wang Yun;Li Qiong;Xiao Yi;Tian Jie;Liu Shiyuan(Department of Imaging, Changzheng Hospital, Second Military Medical University, Shanghai 200003, Chin)

机构地区：[1]第二军医大学长征医院影像科,上海200003 [2]中国科学院自动化研究所分子影像重点实验室

出　　处：《中华放射学杂志》2017年第12期912-917,共6页Chinese Journal of Radiology

基　　金：国家重点研发计划政府间合作项目（2016YFE0103000,2017YFC1308703,2017YFC1308700）;国家自然科学基金（81370035,81771924）;上海市浦江人才计划（15PJD002）

摘　　要：目的建立并验证影像组学鉴别表现为磨玻璃结节（GGN）的浸润性肺腺癌与＂非＂浸润性肺腺癌的能力，并与形态学特征和定量影像进行对照。 方法2011年11月至2014年12月纳入160例病理证实的肺腺癌为原始训练数据集，搜集2014年11月至2015年12月76例孤立GGN作为独立验证集。采用LASSO回归分析方法进行特征选择和影像组学标签建立。利用选择特征的线性融合计算每例患者的组学标签得分。多参数回归分析用于模型的建立。ROC曲线及曲线下面积（AUC）用于评价单个特征及模型的预测效能，并使用Delong检验比较各模型之间效能是否具有显著差异。留一法交叉验证评估模型的泛化能力。校正曲线用于评价列线图的校正效果，并使用Hosmer-Lemeshow检验分析风险率预测值和观测概率之间是否存在显著性差异。 结果共提取了485个三维特征，通过降维发现2个特征是最重要的鉴别诊断因子并建立了影像组学标签。个体化预测模型由年龄、影像组学标签、毛刺征和胸膜凹陷征组成，与其他模型和平均CT值相比，具有最佳的诊断效能（AUC=0.934），高于临床模型（AUC=0.743，P〈0.001）。基于影像组学的列线图在训练集和验证集中均具有较好的校正效能，而且在验证集中的鉴别诊断效能更高（AUC=0.956）。 结论由年龄、影像组学标签、毛刺征和胸膜凹陷征组成的个体化预测模型，并通过列线图表示，能有效鉴别浸润性腺癌和＂非＂浸润性腺癌，与形态学模型和定量影像相比，具有最好的预测效能。ObjectiveTo develop and validate the radiomics nomogram on the discrimination of lung invasive adenocarcinoma from ＇non-invasive’ lesion manifesting as ground glass nodule （GGN） and compare it with morphological features and quantitative imaging. MethodsOne hundred and sixty pathologically confirmed lung adenocarcinomas from November 2011 to December 2014 were included as primary cohort. Seventy-six lung adenocarcinomas from November 2014 to December 2015 were set as an independent validation cohort. Lasso regression analysis was used for feature selection and radiomics signature building. Radiomics score was calculated by the linear fusion of selected features. Multivariable logistic regression analysis was performed to develop models. The prediction performances were evaluated with ROC analysis and AUC, and the different prediction performance between different models and mean CT value were compared with Delong test. The generalization ability was evaluated with the leave-one-out cross-validation method. The performance of the nomogram was evaluated in terms of its calibration. The Hosmer-Lemeshow test was used to evaluate the significance between the predictive and observe values. ResultsFour hundred and eighty-five 3D features were extracted and reduced to 2 features as the most important discriminators to build the radiomics signatures. The individualized prediction model was developed with age, radiomics signature, spiculation and pleural indentation, which had the best discrimination performance （AUC=0.934） in comparison with other models and mean CT value（P〈0.05） and showed better performance compared with the clinical model （AUC=0.743, P〈0.001）. The radiomics-based nomogram demonstrated good calibration in the primary and validation cohort, and showed improved differential diagnosis performance with an AUC of 0.956 in the independent validation cohort. ConclusionIndividualized prediction model incorporating with age, radiomics signature, spiculation and pleural indentatio

关 键 词：肺肿瘤 磨玻璃结节 影像组学 诊断 

分 类 号：R730.44[医药卫生—肿瘤] R734.2[医药卫生—临床医学]