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©The Author(s) 2024.
World J Gastrointest Oncol. Mar 15, 2024; 16(3): 857-874
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.857
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.857
Figure 5 Receiver operating characteristic of the combined radiomics models.
A: Receiver operating characteristic (ROC) of combined radiomics model using four machine learning algorithms on the training set; B: ROC of combined radiomics model using four machine learning algorithms on the internal test set; C: ROC of combined radiomics model using four machine learning algorithms on the external test set. ROC: Receiver operating characteristic; LR: Logistic regression; SVM: Support vector machine; DT: Decision tree; RF: Random forest; CI: Confidence interval; AUC: Area under the curve.
- Citation: Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16(3): 857-874
- URL: https://www.wjgnet.com/1948-5204/full/v16/i3/857.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i3.857