Copyright: ©Author(s) 2026.
Figure 5 Receiver operating characteristic curves of clinical, radiomics, and combined models in training and testing cohorts.
A: Receiver operating characteristic curves for the clinical model, random forest radiomics model, and combined nomogram model in the training cohort, illustrating comparative predictive performance; B: Receiver operating characteristic curves for the clinical model, random forest radiomics model, and combined nomogram model in the testing cohort, demonstrating model performance on independent data. Clinic: Clinical model; ALL: Random forest radiomics model; Nomogram: Combined model; AUC: Area under the curve.
- Citation: Zheng ZH, Wu CH, Hu JB, Xu JF, Zi XY, Chen JH, He Q, Dong WY. Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma. World J Radiol 2026; 18(7): 121161
- URL: https://www.wjgnet.com/1949-8470/full/v18/i7/121161.htm
- DOI: https://dx.doi.org/10.4329/wjr.121161