Copyright
©The Author(s) 2024.
World J Gastrointest Oncol. Mar 15, 2024; 16(3): 819-832
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.819
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.819
Figure 7 The nomogram integrates clinical and radiomic features.
In the two factors of "nerve invasion" and "vascular invasion", "0" represents absent, "1" represents present, and in "circumference", "0" represents ≤ 1/2, "1" represents > 1/2.
- Citation: Zheng HD, Huang QY, Huang QM, Ke XT, Ye K, Lin S, Xu JH. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma. World J Gastrointest Oncol 2024; 16(3): 819-832
- URL: https://www.wjgnet.com/1948-5204/full/v16/i3/819.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i3.819