基于血清学标记物和CT特征模型预测肝细胞癌组织分化程度  

Serological markers and CT features-based model for predicting histological grade of hepatocellular carcinoma

作　　者：黄翔 何畅 陈莲环 凌文峰[1] 张志强[1] 朱志强[1] 陈小凤 杨志企[1] HUANG Xiang;HE Chang;CHEN Lianhuan;LING Wenfeng;ZHANG Zhiqiang;ZHU Zhiqiang;CHEN Xiaofeng;YANG Zhiqi(Department of Radiology,Meizhou People's Hospital,Meizhou 514031,China;Department of Radiology,Meizhou Railway Bridge Hospital,Meizhou 514031,China)

机构地区：[1]梅州市人民医院影像科,广东梅州514031 [2]梅州铁炉桥医院影像科,广东梅州514031

出　　处：《分子影像学杂志》2024年第6期649-654,共6页Journal of Molecular Imaging

基　　金：广东省医学科研基金项目(B2023445);梅州市人民医院培育项目(PY-C2023035);梅州市社会发展科技计划项目(2023B12)。

摘　　要：目的探讨基于联合血清学标记物和CT特征模型在评估肝细胞癌(HCC)组织分化程度中的应用价值。方法回顾性收集梅州市人民医院2015年11月~2023年10月共206例HCC患者的临床及CT资料,其中训练组144例(包括42例低分化HCC和102例中高分化HCC),验证组62例(包括21例低分化HCC和41例中高分化HCC)。比较低分化HCC和中高分化HCC组间血清学标记物及CT特征差异。采用多因素筛选HCC分化程度独立危险因素并构建模型。结果相比中高分化HCC,低分化HCC的AFP阳性率(P=0.001)、乙肝发生率(P=0.003)、低密度环征(P=0.015)和癌栓发生率(P=0.001)较高,平扫CT值较低(P=0.010)。多因素分析显示AFP(OR=0.269,P=0.027)、低密度环征(OR=0.273,P=0.047)、癌栓(OR=0.191,P=0.005)和肿瘤平扫CT值(OR=1.091,P=0.009)是HCC组织分化程度的独立危险因素。基于联合AFP、低密度环征、癌栓和肿瘤平扫CT值的联合模型诊断效能最高,其在训练组和验证组中的曲线下面积分别为0.780和0.620。结论AFP、低密度环征、癌栓和肿瘤平扫CT值是HCC组织分化程度的独立危险因素,基于上述特征构建的联合模型对HCC组织分化程度具有较好诊断价值。Objective To ascertain utility of the model that combines serum markers and CT features in assessing the differentiation degree of hepatocellular carcinoma(HCC).Methods A total of 206 cases of HCC clinical and CT data were collected retrospectively and the patients were divided into training set(including 42 cases of low-differentiated HCC and 102 cases of middle-high differentiated HCC)and testing set(including 21 cases of low-differentiated HCC and 41 cases of middle-high differentiated HCC).The underlying differences between low-differentiated HCC group and middle-high differentiated HCC group in terms of clinical and CT features were meticulously compared.Applying multivariate Logistic regression,we isolated independent risk factors for HCC differentiation degree and construct the prediction models.Results Compared with medium-high differentiated HCC,low-differentiated HCC had statistically significant higher rate of AFP positivity(P=0.001),occurrences of hepatitis B(P=0.003),low-density ring sign(P=0.015),cancer thromboembolism(P=0.001),and lower CT values during plain scan(P=0.010).Further multivariate logistic regression analysis revealed that AFP(OR=0.269,P=0.027),low-density ring sign(OR=0.273,P=0.047),cancer thromboembolism(OR=0.191,P=0.005),and plain scan CT value of tumor(OR=1.091,P=0.009)act as risk factors for HCC differentiation degree.The optimal diagnostic performance was achieved by the model that integrated AFP,low-density ring sign,cancer thromboembolism,and CT value of tumor during plain scan,as demonstrated by the area under the curve of 0.780 and 0.620 in the training and testing set,respectively.Conclusion AFP,low-density ring sign,cancer thromboembolism,and CT value of tumor during plain scan are independent risk factors for the differentiation degree of HCC tissue.when amalgamated into the model,the joint model constructed based on these features can provide a high-accuracy diagnosis for HCC differentiation degree.

关 键 词：肝细胞癌 组织分化程度 CT 血清学标记物 诊断效能 

分 类 号：R735.7[医药卫生—肿瘤] R730.44[医药卫生—临床医学]