建立并验证基于 CT 的列线图，以准确检测高危患者的 HCC。2017 年 1 月至 5 月期间，共有 223 名患者被分为训练组 (n=161) 和验证组 (n=62) 2022年进行Logistic分析，并分别开发临床模型和放射学模型。最后，根据临床和放射学特征建立列线图。所有模型均使用曲线下面积 (AUC) 进行评估。 DeLong 检验用于评估这些模型之间的差异。 在多变量分析中，性别（p = 0.014）、甲胎蛋白（AFP）增加（p = 0.017）、非边缘动脉期过度增强（APHE）（p = 0.011） ）、冲洗（p = 0.011）和增强胶囊（p = 0.001）是 HCC 的独立差异预测因子。基于这五个因素，用拟合良好的校准曲线形成列线图。训练组和验证组列线图的曲线下面积（AUC）分别为0.961（95% CI：0.935~0.986）和0.979（95% CI：0.949~1）。该列线图在训练和验证队列中优于临床和放射学模型。结合临床和 CT 特征的列线图可以成为检测 HCC 并实现 HCC 高危患者风险分层的简单可靠的工具。版权所有 © 2024 梁吴和魏。

To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease.A total of 223 patients were divided into training (n=161) and validation (n=62) cohorts between January of 2017 and May of 2022. Logistic analysis was performed, and clinical model and radiological model were developed separately. Finally, a nomogram was established based on clinical and radiological features. All models were evaluated using the area under the curve (AUC). DeLong's test was used to evaluate the differences among these models.In the multivariate analysis, gender (p = 0.014), increased Alpha-fetoprotein (AFP) (p = 0.017), non-rim arterial phase hyperenhancement (APHE) (p = 0.011), washout (p = 0.011), and enhancing capsule (p = 0.001) were the independent differential predictors of HCC. A nomogram was formed with well-fitted calibration curves based on these five factors. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.961(95%CI: 0.935~0.986) and 0.979 (95% CI: 0.949~1), respectively. The nomogram outperformed the clinical and the radiological models in training and validation cohorts.The nomogram incorporating clinical and CT features can be a simple and reliable tool for detecting HCC and achieving risk stratification in patients at high risk for HCC.Copyright © 2024 Liang, Wu and Wei.