基于增强CT影像组学列线图预测胸腺上皮性肿瘤侵袭性  

Predicting the Invasiveness of Thymic Epithelial Tumors Based on Enhanced CT Radiomics Imaging Nomogram

作　　者：刘雪成 吴树剑 王娟 韦俊 袁权 LIU Xuecheng;WU Shujian;WANG Juan;WEI Jun;YUAN Quan(Department of Radiology,the First Affiliated Hospital of Wannan Medical College,Wuhu 241000,China)

机构地区：[1]皖南医学院第一附属医院放射科,安徽芜湖241000 [2]皖南医学院第一附属医院胸外科,安徽芜湖241000

出　　处：《中国医学影像学杂志》2024年第10期1014-1020,共7页Chinese Journal of Medical Imaging

基　　金：皖南医学院校重点科研基金项目(WK2022ZF19)。

摘　　要：目的探讨基于增强CT组学列线图预测胸腺上皮性肿瘤侵袭性的价值。资料与方法回顾性收集2015年1月—2023年1月皖南医学院第一附属医院经病理证实的155例胸腺上皮性肿瘤患者的临床及影像学资料,按照7∶3随机分训练集(n=108)及验证集(n=47)。在静脉期图像提取影像组学特征;采用最小绝对收缩和选择算子算法进行降维,筛选最优特征建立影像组学标签,并计算标签得分(Rad-score);使用单因素及多因素回归分析筛选独立危险因素,分别构建影像特征模型、Rad-score和影像组学-临床联合模型,并绘制联合模型列线图;使用受试者工作特征曲线及决策曲线评价模型的诊断效能及临床收益;比较模型间曲线下面积差异;使用校正曲线评价列线图的校准度。结果经降维筛选出16个最优影像组学特征。Logistic回归分析发现肿瘤形态(OR=2.932,P=0.025)、周围组织侵犯(OR=11.461,P=0.005)及Rad-score(OR=255.27,P=0.002)为独立危险因素。联合模型列线图的曲线下面积在训练集及验证集分别为0.852、0.831;与影像特征模型及Rad-score在训练集比较差异均有统计学意义(Z=3.607、2.270,P<0.05)。列线图模型训练集的阈值概率在0.08~0.88时临床获益。结论基于增强CT影像组学结合临床特征的联合模型列线图能够有效预测胸腺上皮性肿瘤的侵袭性,可辅助临床术前制订精准的治疗方案。Purpose Explore the predictive value of nomograms based on enhanced CT radiomics for invasiveness of thymic epithelial tumor.Materials and Methods The clinical and imaging data from 155 cases confirmed with thymic epithelial tumors at the First Affiliated Hospital of Wannan Medical College from January 2015 to January 2023 were retrospectively collected.All cases were randomly divided into training(n=108)and validation(n=47)groups in a 7∶3 ratio.The radiomics features from venous phase images were extracted.The least absolute shrinkage and selection operator algorithm for dimensionality reduction were utilized to establish radiomics labels and calculate the Rad-score.Univariate and multivariate regression analyses were conducted to identify independent risk factors.Imaging feature models,Rad-score and imaging omics clinical combined model were constructed to plot the corresponding nomograms.The diagnostic performance and clinical benefits of the models were evaluated via receiver operating characteristic curves and decision curves.The DeLong test was applied to compare area under the curve differences between models and used calibration curves to assess nomograms calibration.Results 16 optimal image omics features were selected by dimensionality reduction.Logistic regression analysis showed that tumor morphology(OR=2.932,P=0.025),peripheral tissue invasion(OR=11.461,P=0.005)and Rad-score(OR=255.27,P=0.002)were independent risk factors.The area under the curve in the training set and the verification set were 0.852 and 0.831,respectively.Compared with the image feature model and Rad-score in the training set,the differences were statistically significant(Z=3.607,2.270,P<0.05).The threshold probability of the column chart model training set was between 0.08 and 0.88 for clinical benefit.Conclusion The combined model nomograms based on enhanced CT radiomics and clinical features can effectively predict thymic epithelial tumor invasiveness and assist clinicians in formulating precise treatment plans before surgery.

关 键 词：胸腺上皮瘤 胸腺瘤 体层摄影术 X线计算机 影像组学 列线图表 病理学 外科 

分 类 号：R736.3[医药卫生—肿瘤] R445.3[医药卫生—临床医学]