基于CT影像特征的列线图模型预测小肠间质瘤病理危险度分级  

A nomogram model based on CT imaging features to predict the pathological risk classification of small intestinal stromal tumors

作　　者：许莹 植伟华 李璐 滕泽 张慧勤 叶枫 赵心明 Xu Ying;Zhi Weihua;Li Lu;Teng Ze;Zhang Huiqin;Ye Feng;Zhao Xinming(Department of Diagnostic Radiology,National Cancer Center,National Clinical Research Center for Cancer,Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021,China)

机构地区：[1]国家癌症中心,国家肿瘤临床医学研究中心,中国医学科学院北京协和医学院肿瘤医院影像诊断科,北京100021

出　　处：《中华放射学杂志》2024年第10期1063-1068,共6页Chinese Journal of Radiology

摘　　要：目的探讨基于小肠间质瘤(SIST)患者术前CT特征构建的影像列线图模型预测病理危险度分级的价值。方法该研究为队列研究,回顾性收集2014年1月至2023年10月于中国医学科学院肿瘤医院经术后病理证实的SIST患者142例。根据2008年改良版美国国立卫生组织分级标准,将患者分为病理中/高危组(86例)和极低/低危组(56例)。分析SIST术前增强CT影像特征,包括形状、边界、坏死、瘤内出血、瘤内钙化、生长方式、强化形式、强化程度、肿瘤供血或引流血管、肿瘤位置。对患者进行随访,确定无复发生存期(RFS)。采用单因素及多因素logistic回归在影像学特征中筛选病理中/高危组SIST的独立预测因素,将独立预测因素联合构建影像预测模型,并绘制列线图。使用受试者操作特征曲线评估模型的预测效能,使用Kaplan-Meier法绘制生存曲线,以log-rank检验比较RFS的差异。结果单因素logistic回归结果显示形状、坏死、瘤内出血、肿瘤供血或引流血管、肿瘤位置差异有统计学意义(P<0.05),多因素logistic回归结果显示肿瘤形状(OR=3.92,95%CI 1.58~9.71,P=0.003)、坏死(OR=4.60,95%CI 1.91~11.09,P<0.001)、肿瘤供血或引流血管(OR=6.25,95%CI 1.74~22.47,P=0.005)是病理中/高危组的独立预测因素。联合三者的影像预测模型预测SIST中/高危组的曲线下面积为0.835(95%CI 0.769~0.901),灵敏度为0.810,特异度为0.839,准确度为0.789。以截断值(0.810)作为界值,根据预测结果将患者分为影像预测高危组(74例)和影像预测低危组(68例),影像预测高危组的中位RFS低于影像预测低危组的RFS,差异有统计学意义(χ^(2)=5.20,P=0.023)。结论基于术前CT影像特征形状、坏死、肿瘤供血或引流血管的影像列线图模型可在术前有效预测病理中/高危组SIST,并对患者术后复发具有预测价值。Objective To explore the value of the imaging nomogram model based on preoperative CT features of patients with small intestinal stromal tumor(SIST)in predicting pathological risk classification.Methods This was a cohort study.The patients who were diagnosed as primary SIST by postoperative pathology in Cancer Hospital,Chinese Academy of Medical Sciences from January 2014 to October 2023 were retrospectively included.According to the modified 2008 National Institutes of Health classification criteria,the patients were divided into a pathological intermediate/high-risk group(86 cases)and a very low/low-risk group(56 cases).The features of preoperative enhanced CT images of SIST were analyzed,including tumor boundary,necrosis,intra-tumoral hemorrhage,intra-tumoral calcification,growth pattern,enhancement pattern,enhancement degree,enlarged vessels feeding or draining the mass(EVFDM),and tumor location.Patients were followed up to determine the recurrence-free survival(RFS).Univariate and multivariate logistic regression were used to screen the independent predictors of SIST with pathological medium/high-risk group.The independent predictors were combined to construct an imaging prediction model,and a nomogram was drawn.The receiver operating characteristic curve was used to evaluate the predictive efficacy of the model.The Kaplan-Meier method was used to draw the survival curve,and the log-rank test was used to compare the differences in RFS.Results Univariate logistic regression results showed that tumor shape,necrosis,intra-tumoral hemorrhage,EVFDM,and tumor location were potentially related to medium/high-risk SIST.Multivariate logistic regression results showed that tumor shape(OR=3.92,95%CI 1.58-9.71,P=0.003),necrosis(OR=4.60,95%CI 1.91-11.09,P<0.001),and EVFDM(OR=6.25,95%CI 1.74-22.47,P=0.005)were independent predictors of pathological intermediate/high-risk SIST.The area under the curve of the imaging predictive model combining the three predictors to predict the intermediate/high-risk SIST was 0.835(95%CI 0

关 键 词：胃肠道间质肿瘤 体层摄影术 X线计算机 病理分级 预测模型 

分 类 号：R735.32[医药卫生—肿瘤] R730.44[医药卫生—临床医学]