Copyright: ©Author(s) 2026.
World J Gastrointest Oncol. Apr 15, 2026; 18(4): 115635
Published online Apr 15, 2026. doi: 10.4251/wjgo.v18.i4.115635
Published online Apr 15, 2026. doi: 10.4251/wjgo.v18.i4.115635
Figure 4 Comparative analysis of area under the receiver operating characteristic curve for the nine models.
A: Comparison of the area under the receiver operating characteristic curve of the nine models in the training cohort; B: Comparison of the area under the receiver operating characteristic curve of the nine models in the internal validation cohort. ROC: Receiver operating characteristic; AUC: Area under the receiver operating characteristic curve; T: The intratumoral model; P3: The peritumoral-3mm model; TP3: The intra-peritumoral-3mm model; R: The radiomics model; DLR: The deep learning radiomics model; DLRR: Deep learning-radiomics integrated model.
- Citation: Lei XD, Qian GX, Sun ZG, Tang ZQ, Liu YC, Du R, Li YH. Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer. World J Gastrointest Oncol 2026; 18(4): 115635
- URL: https://www.wjgnet.com/1948-5204/full/v18/i4/115635.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v18.i4.115635