Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Department of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu Distinct, 58 Zhongshan Road 2, Guangzhou, 500018, China.
BMC Cancer. 2022 Jun 28;22(1):709. doi: 10.1186/s12885-022-09812-w.
With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP.
A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance.
Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P < 0.01). A clinical model (including 3 major features, TNSI, gender and AFP) was built to diagnose HCC and achieved good diagnostic performance (area under curve values were 0.954 in the training cohort, 0.931 in the internal validation cohort and 0.902 in the external cohort).
The clinical model in this study developed a satisfied non-invasive diagnostic performance for HCC in LRP.
由于低危人群（LRP）中肝细胞癌（HCC）的患病率，因此在该人群中建立一种非侵入性诊断策略变得越来越迫切，以免对不必要的活检进行不必要的活检。本研究的目的是找到 HCC 的特征，并建立一种合适的非侵入性方法来诊断 LRP 中的 HCC。
共收集了来自 2 个机构的 681 名 LRP 患者（定义为无肝硬化，慢性乙型肝炎病毒感染或 HCC 病史的人群）。对计算机断层扫描（CT）和磁共振成像（MRI）的图像进行了手动分析。我们根据第一家机构的入院时间将患者分为训练队列（n = 324）和内部验证队列（n = 139）。第二家机构的队列被视为外部验证队列（n = 218）。建立了一个包含影像学和临床独立危险因素的多变量逻辑回归模型。C 统计量用于评估诊断性能。
除了 HCC 的主要影像学特征（非边缘增强，洗脱和增强包膜）外，影像学上的肿瘤坏死或严重缺血（TNSI）和两个临床特征（性别和甲胎蛋白）也与 HCC 诊断独立相关（均 P <0.01）。建立了一个临床模型（包括 3 个主要特征，TNSI，性别和 AFP）来诊断 HCC，并取得了良好的诊断性能（训练队列的曲线下面积值为 0.954，内部验证队列为 0.931，外部队列为 0.902）。
本研究中的临床模型为 LRP 中 HCC 开发了一种令人满意的非侵入性诊断性能。
