Abstract | Objectives: Methods: 202 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). Results: The 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 α- 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. Conclusions:
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Authors | Tingting Fan, Shijie Li, Kai Li, Jingxu Xu, Sheng Zhao, Jinping Li, Xinglu Zhou, Huijie Jiang |
Journal | Frontiers in oncology
(Front Oncol)
Vol. 12
Pg. 857715
( 2022)
ISSN: 2234-943X [Print] Switzerland |
PMID | 35444942
(Publication Type: Journal Article)
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Copyright | Copyright © 2022 Fan, Li, Li, Xu, Zhao, Li, Zhou and Jiang. |