CT影像组学-临床指标联合模型早期预测急性胰腺炎严重程度  

CT radiomics and clinical indicators combined model in early prediction the severity of acute pancreatitis

作　　者：徐丹丹 肖奥齐 杨维森[1] 顾燕 金丹[1] 殷国建[2] 印宏坤 范国华[1] 沈钧康[1] 徐亮[1] Xu Dandan;Xiao Aoqi;Yang Weisen;Gu Yan;Jin Dan;Yin Guojian;Yin Hongkun;Shen Junkang;Fan Guohua;Xu Liang(Radiology Department,The Second Affiliated Hospital of Soochow University,Suzhou,215004,China;Gastroenterology Department,The Second Affiliated Hospital of Soochow University,Suzhou,215004,China;Infervision Medical Technology Co.,Ltd,Beijing 100025,China)

机构地区：[1]苏州大学附属第二医院放射科,苏州215004 [2]苏州大学附属第二医院消化科,苏州215004 [3]北京推想医疗科技有限公司,北京100025

出　　处：《中华急诊医学杂志》2024年第10期1383-1389,共7页Chinese Journal of Emergency Medicine

摘　　要：目的探讨CT影像组学联合临床指标建立的Nomogram模型对早期预测急性胰腺炎(acute pancreatitis,AP)严重程度的价值。方法回顾性收集苏州大学附属第二医院自2016年1月至2023年3月的AP患者,根据2012年修订版亚特兰共识分为重症组和非重症组。所有患者均为初次发病,且1周内完善腹部CT平扫及增强检查。将患者按7:3的比例随机分成训练组和验证组。在各期CT图像上勾画胰腺实质作为感兴趣区,并通过python软件提取影像组学特征,使用LASSO回归及10折交叉验证法降维、筛选最优特征,建立影像组学标签。临床资料及实验室指标采用多因素Logistic回归筛选出重症急性胰腺炎(severe acute pancreatitis,SAP)的独立预测因子,建立临床模型。联合CT影像组学标签及临床独立预测因子建立Nomogram模型。采用受试者工作特征(receiver operating characteristic,ROC)曲线及决策曲线分析(decision curve analysis,DCA)评估各模型的预测效能。结果最终入组AP患者205例(重症组59例,非重症组146例),从所有AP患者的平扫、动脉期、静脉期及延迟期CT图像上各筛选出3、5、5、5个最优影像组学特征建模,其中动脉期组学模型预测SAP效能相对更好,在训练组和验证组中的ROC曲线下面积(area under curve,AUC)分别为0.937和0.913。多因素Logistic回归显示C-反应蛋白(C-reactive protein,CRP)和乳酸脱氢酶(lactate dehydrogenase,LDH)是SAP的独立预测因子,两者共同建立临床模型,在训练组和验证组中的AUC分别为0.879和0.889。基于动脉期影像组学标签、CRP和LDH建立Nomogram联合模型,其在训练组和验证组中AUC分别为0.956和0.947。DCA显示Nomogram模型的净收益高于单独的临床模型及影像组学模型。结论CT影像组学联合临床指标建立的Nomogram模型对于早期预测AP病情的严重程度具有较高的应用价值,有利于临床治疗方案的制定与预后评估。Objective To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis(AP).Methods From January 2016 to March 2023,the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected.According to the revised Atlanta classification and definition of acute pancreatitis in 2012,all patients were divided into the severe group and the non-severe group.All patients were first diagnosed,and abdominal CT plain scan and enhanced scan were completed within 1 week.Patients were randomly(random number)divided into training and validation groups at a ratio of 7:3.The pancreatic parenchyma was delineated as the region of interest on each phase CT images,and the radiomics features were extracted by python software.LASSO regression and 10-fold crossvalidation were used to reduce the dimension and select the optimal features to establish the radiomics signature.Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis(SAP),and a clinical model was established.A Nomogram model was established by combining CT radiomics signature and clinical independent predictors.Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive efficacy of each model.Results Total of 205 AP patients were included(59 cases in severe group,146 cases in nonsevere group).3,5,5 and 5 optimal radiomics features were selected from the plain CT scan,arterial phase,venous phase and delayed phase images of all patients,and the radiomics models were established.Among them,the arterial phase radiomics model had relatively better performance in predicting SAP,with an area under curve(AUC)of 0.937 in the training group and 0.913 in the validation group.Multivariate Logistic regression showed that C-reactive protein(CRP)and lactate dehydrogenase(LDH)were independent predictors of SAP,and they were used to establish a clinical model

关 键 词：急性胰腺炎 严重程度 影像组学 预测模型 NOMOGRAM 

分 类 号：R576[医药卫生—消化系统]