Ma Ruixia, Feng Shi-Ting, Zhou Xiaoqi, Chen Meichen, Wang Jifei, Dong Zhi
Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong, China.
Curr Med Imaging. 2024;20:e15734056269369. doi: 10.2174/0115734056269369231213102554.
Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a noncirrhotic liver.
Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging(MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram.
Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability.
In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.
在无肝硬化的患者中,肝脏血管周围上皮样细胞瘤(PEComa)常与肝细胞癌(HCC)相似。本研究旨在利用钆塞酸二钠增强MRI的影像特征建立列线图,以区分非肝硬化肝脏中的PEComa和HCC。
本研究纳入了40例接受钆塞酸二钠增强磁共振成像(MRI)检查的非肝硬化患者。采用多因素逻辑回归模型选择区分PEComa和HCC的显著变量。基于回归模型建立列线图。通过ROC曲线和校准曲线评估列线图的性能。进行决策曲线分析(DCA)以评估列线图的临床实用性。
确定了两个显著预测因素:早期引流静脉的出现和肿瘤的T1D值。ROC曲线显示,预测PEComa风险模型的曲线下面积(AUC)为0.91(95%CI:0.80~1),表明该模型具有高特异性(92.3%)和敏感性(88.9%)。纳入这两个预测因素的列线图显示出良好的校准,通过1000次重采样程序进行验证,该模型的校正C指数为0.90。此外,DCA分析表明该模型具有临床实用性。
总之,列线图模型在区分非肝硬化患者的PEComa和HCC方面显示出良好的预测准确性,可能有助于临床决策。