食管鳞状细胞癌新辅助化疗联合免疫治疗后头侧癌旁组织与切缘组织的CT影像组学对比研究  

Comparative analysis of CT radiomics features between cephalic para-carcinoma tissues and resection margin in esophageal squamous cell carcinoma after neoadjuvant chemotherapy and immunotherapy

作　　者：苟月芹 高丹 欧静 陈天武 GOU Yueqin;GAO Dan;OU Jing;CHEN Tianwu(Medical Imaging Key Laboratory of Sichuan Province,Department of Radiology,Affiliated Hospital of North Sichuan Medical College,Nanchong 637000,China;Department of Radiology,Qionglai Central Hospital;Department of Radiology,The Second Affiliated Hospital of Chongqing Medical University)

机构地区：[1]医学影像四川省重点实验室川北医学院附属医院放射科,南充637000 [2]邛崃市医疗中心医院放射科 [3]重庆医科大学附属第二医院放射科

出　　处：《国际医学放射学杂志》2025年第2期125-131,共7页International Journal of Medical Radiology

基　　金：国家自然科学基金(82271959)。

摘　　要：目的探讨基于增强CT(CECT)的影像组学模型鉴别新辅助化疗联合免疫治疗(NACI)后食管鳞状细胞癌(ESCC)头侧癌旁组织与切缘组织的可行性。方法回顾性收集2家医疗中心的188例接受NACI并经病理证实的ESCC病人的CECT特征和临床资料。将来自A中心的138例病人按7∶3的比例随机分为训练集(97例)和内部验证集(41例),来自B中心的50例病人作为外部验证集。采用3D-Slicer开源软件在CECT影像勾画4个头侧癌旁组织(P1、P2、P3和P4)与切缘组织(P5)的感兴趣区(ROI)(分别距肿瘤上缘1、2、3、4、5 cm),Pyrdiomics软件包提取影像组学特征。将4处头侧癌旁组织获得的特征分别与切缘组织特征进行配对,用以鉴别两者,即P1、P2、P3、P4组。采用单因素分析、最小绝对收缩和选择算子(LASSO)算法筛选训练集中的最优影像组学特征,构建Logistic回归模型。采用受试者操作特征(ROC)曲线下面积(AUC)评估影像组学模型的鉴别效能。结果在训练集、内部验证集及外部验证集中,P1组模型的AUC分别为0.831、0.820、0.787,P2组模型的AUC分别为0.809、0.797、0.769。P1组、P2组模型均有较好的鉴别效能(均AUC>0.76),且P1组模型的AUC值均分别大于P2组模型。结论基于增强CT影像组学模型对鉴别NACI后ESCC头侧癌旁组织(P1和P2)与切缘组织具有较好的效能。Objective To explore the feasibility of radiomics models based on contrast-enhanced computed tomography(CECT)in distinguishing cephalic para-carcinoma tissues and resection margin in esophageal squamous cell carcinoma(ESCC)following neoadjuvant chemotherapy and immunotherapy(NACI).Methods This retrospective study included 188 pathologically confirmed ESCC patients who underwent NACI,recruited from two medical centers.A total of 138 patients from Center A were randomly divided into a training set(97 cases)and an internal validation set(41 cases)at a 7∶3 ratio,while 50 patients from Center B served as an external validation set.Using an open-source software 3D-Slicer,four regions of interest(ROIs)representing cephalic para-carcinoma tissues(P1,P2,P3,and P4)at 1 cm,2 cm,3 cm,and 4 cm above the tumor margin,respectively,and one ROI for resection margin tissue(P5,5 cm above the tumor)were delineated on CECT images.Radiomics features were extracted using the Pyradiomics package.The radiomics features obtained from four cephalic para-carcinoma tissues were individually paired with those of resection margin tissue to differentiate between them,which were designated as groups P1,P2,P3,and P4,respectively.Univariate analysis and the least absolute shrinkage and selection operator(LASSO)method were employed to select optimal radiomics features in the training sets,and logistic regression models were constructed.The area under the receiver operating characteristic(ROC)curve(AUC)was used to assess the discriminatory performance of the radiomics models.Results The AUCs of the P1 model in the training,internal validation,and external validation sets were 0.831,0.820,and 0.787,respectively.The AUCs of the P2 model were 0.809,0.797,and 0.769,respectively.Both the P1 and P2 models demonstrated good discriminatory performance(AUC>0.76),with the P1 model achieving higher AUC values than the P2 model in all datasets.Conclusion The CECT-based radiomics model demonstrates high efficacy in distinguishing cephalad peritumoral(P1 and P2

关 键 词：食管鳞状细胞癌 新辅助化疗 免疫治疗 体层摄影术 X线计算机 影像组学 

分 类 号：R814.42[医药卫生—影像医学与核医学] R816.5[医药卫生—放射医学]