Hepatic Pecoma versus Hepatocellular Carcinoma In The Noncirrhotic Liver on Gd-EOB-DTPA-Enhanced MRI: A Diagnostic Challenge.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Current Medical Imaging Reviews Pub Date : 2024-02-27 DOI:10.2174/0115734056269369231213102554
Ruixia Ma, Shi-Ting Feng, Xiaoqi Zhou, Meichen Chen, Jifei Wang, Zhi Dong
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Abstract

Aim: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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Gd-EOB-DTPA 增强核磁共振成像显示非肝硬化肝脏中的肝绒毛膜癌与肝细胞癌:诊断挑战。
目的:肝血管周围上皮样细胞瘤(PEComa)通常会在无肝硬化的患者中模拟肝细胞癌(HCC)。本研究旨在利用 Gd-EOB-DTPA 增强 MRI 的成像特征制定一个提名图,并将非肝硬化肝脏中的 PEComa 与 HCC 区分开来:研究纳入了40例非肝硬化Gd-EOB-DTPA增强磁共振成像(MRI)患者。采用多变量逻辑回归模型来选择区分 PEComa 和 HCC 的重要变量。根据回归模型制定了一个提名图。根据 ROC 曲线和校准曲线评估了提名图的性能。为评估提名图的临床实用性,还进行了决策曲线分析(DCA):结果:发现了两个重要的预测因素:出现早期引流静脉和肿瘤的 T1D 值。ROC 曲线显示,预测 PEComa 风险的模型的曲线下面积(AUC)为 0.91(95% CI:0.80~1),并显示该模型具有较高的特异性(92.3%)和灵敏度(88.9%)。包含这两个预测因子的提名图显示出良好的校准性,并通过 1000 次重采样程序进行了验证,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,该提名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,有助于临床决策。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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