A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature

被引:14
作者
Fan, Tingting [1 ]
Li, Shijie [2 ]
Li, Kai [2 ]
Xu, Jingxu [3 ]
Zhao, Sheng [1 ]
Li, Jinping [1 ]
Zhou, Xinglu [4 ]
Jiang, Huijie [1 ]
机构
[1] Harbin Med Univ, Dept Radiol, Affiliated Hosp 2, Harbin, Peoples R China
[2] Harbin Med Univ, Dept Intervent Radiol, Canc Hosp, Harbin, Peoples R China
[3] Beijing Deepwise & League Technol Co Ltd, Res & Dev R&D Ctr, Dept Res Collaborat, Beijing, Peoples R China
[4] Harbin Med Univ, Computed Tomog PET CT Ctr, Dept Positron Emiss Tomog, Canc Hosp, Harbin, Peoples R China
基金
中国国家自然科学基金;
关键词
magnetic resonance imaging; hepatocellular carcinoma; radiomics; VEGF; diagnosis; ENDOTHELIAL GROWTH-FACTOR; EXPRESSION; ANGIOGENESIS; PREDICTION; SURVIVAL; AREA; MRI; CT;
D O I
10.3389/fonc.2022.857715
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
ObjectivesThe objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation. Methods202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant's MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC). ResultsThe entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors alpha-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets. ConclusionsThe combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.
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页数:12
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