基于超声影像组学模型对肝细胞癌肿瘤分化等级的评估价值  

Evaluation value of differentiation grade of hepatocellular carcinoma based on ultrasound imaging omics model

作　　者：郭明珍 王俪洁[1] 唐佳盈 Guo Mingzhen;Wang Lijie;Tang Jiaying(Department of Ultrasound Medicine,Guangyuan First People’s Hospital,Sichuan Guangyuan 628000,China)

机构地区：[1]广元市第一人民医院超声医学科,四川广元628000

出　　处：《中国肝脏病杂志（电子版）》2024年第3期8-16,共9页Chinese Journal of Liver Diseases：Electronic Version

摘　　要：目的探究肝细胞癌(hepatocellular carcinoma,HCC)不同分化等级患者的超声影像组学特征及其对肿瘤分化等级的诊断价值。方法选择2021年3月至2023年3月于广元市第一人民医院就诊并经穿刺活检或手术证实为HCC的212例患者为研究对象纳入训练集,根据肿瘤分化等级将患者分为高分化组(Ⅰ、Ⅱ级,138例)和低分化组(Ⅲ、Ⅳ级,74例),比较两组患者的临床资料,包括性别、年龄、吸烟、饮酒、肝癌家族史、肿瘤直径、病变部位、临床分期、Child-Pugh分级、乙型肝炎、肝硬化、肿瘤包膜、淋巴结肿大、天门冬氨酸氨基转移酶(aspartate aminotransferase,AST)、丙氨酸氨基转移酶(alanine aminotransferase,ALT)、总胆红素、白蛋白、血小板、Ki-67、甲胎蛋白(alpha fetoprotein,AFP)。按照相同纳入与排除标准另选取本院同期收治的60例HCC患者纳入验证集,用于模型的外部验证。采用多因素Logistic回归分析患者肿瘤低分化的影响因素;采集患者的超声图像并提取影像组学特征,采用LASSO回归算法筛选与肿瘤分化等级高度相关的超声影像组学特征(F),并获得其系数(α);采用受试者工作特征(receiver operator characteristic,ROC)曲线分析临床参数模型、超声影像组学评分模型及联合模型的效能;采用R软件构建预测HCC患者肿瘤低分化的列线图模型,采用ROC曲线评价列线图模型的区分度,分别采用校准曲线和临床决策曲线评价列线图模型的准确性和有效性。结果Logistic回归分析表明肝硬化(OR=1.720,95%CI:1.183~2.311,P=0.010)、血小板≥183.69×10^(9)/L(OR=1.418,95%CI:1.051~1.932,P=0.025)、Ki-67阳性(OR=1.552,95%CI:1.363~1.770,P=0.017)、AFP阳性(OR=2.021,95%CI:1.230~2.786,P<0.001)是HCC患者肿瘤低分化的危险因素,AST≥55.14 U/L为保护因素(OR=0.511,95%CI:0.119~0.878,P=0.002)。经LASSO回归算法共筛选出9个超声影像组学特征,超声影像组学评分=-1.071+∑_(i-1)^(9)a_(i)×F_(i)。训Objective To explore the ultrasonographic features of hepatocellular carcinoma(HCC)patients with different differentiation grades and its diagnostic value on poor differentiation of tumors.Methods A total of 212 patients with HCC confirmed by biopsy or surgery in the First People’s Hospital of Guangyuan from March 2021 to March 2023 were selected as the research objects and included in the training set.According to the tumor differentiation grade,the patients were divided into well-differentiated group(gradeⅠandⅡ,138 cases)and poorly differentiated group(gradeⅢandⅣ,74 cases).The clinical data of patients in two groups were compared,including gender,age,smoking,drinking,family history of HCC,tumor diameter,lesion location,clinical stage,Child-Pugh classification,hepatitis B,liver cirrhosis,tumor capsule,lymph node enlargement,aspartate aminotransferase(AST),alanine aminotransferase(ALT),total bilirubin,albumin,platelet,Ki-67 and alpha fetoprotein(AFP).According to the same inclusion and exclusion criteria,60 HCC patients admitted to our hospital during the same period were selected into the validation set for external validation of the model.Multivariate Logistic regression was used to analyze the influencing factors of poor tumor differentiation.The ultrasound images of the patients were collected and the radiomics features were extracted.The LASSO regression algorithm was used to screen the ultrasound radiomics features(F)which were highly related to the tumor differentiation grade,and its coefficient(α)was obtained.The receiver operator characteristic(ROC)curve was used to analyze the efficacy of the clinical parameter model,the ultrasound radiomics score model and the combined model.R software was used to construct a nomogram model for predicting poor differentiation of HCC patients.ROC curve was used to evaluate the discrimination of the nomogram model,and calibration curve and clinical decision curve were used to evaluate the accuracy and effectiveness of the nomogram model.Results Logistic regres

关 键 词：肝细胞癌 肿瘤分化等级 超声影像组学 LASSO回归算法 列线图预测模型 

分 类 号：R735.7[医药卫生—肿瘤] R445.1[医药卫生—临床医学]