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©The Author(s) 2025.
World J Gastroenterol. Aug 14, 2025; 31(30): 109186
Published online Aug 14, 2025. doi: 10.3748/wjg.v31.i30.109186
Published online Aug 14, 2025. doi: 10.3748/wjg.v31.i30.109186
Figure 1 Study population and workflow.
MVI: Microvascular invasion; CT: Computed tomography; HCC: Hepatocellular carcinoma; ROI: Region of interest; CNN: Convolutional neural network; PLH: Predictive likelihood histogram; BoW: Bag-of-word; TF-IDF: Term frequency-inverse document frequency; MLP: Multilayer perceptron; AUC: Area under the curve; DCA: Decision curve analysis; RFS: Recurrence-free survival; PFS: Progression-free survival.
- Citation: Cen YY, Nong HY, Huang XX, Lu XX, Pu CH, Huang LH, Zheng XJ, Pan ZL, Huang Y, Ding K, Huang DY. Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma. World J Gastroenterol 2025; 31(30): 109186
- URL: https://www.wjgnet.com/1007-9327/full/v31/i30/109186.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i30.109186