T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

doi:10.4251/wjgo.v16.i3.819

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastrointest Oncol. Mar 15, 2024; 16(3): 819-832
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.819

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Figure 2 Examples of labelled lesions. A: Primary lesions of colorectal cancer (CRC) on oblique axial T2-weighted images; B: The primary tumour of CRC was drawn at this level, and the range of the red curve indicates the range of the primary tumour at this level.

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

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 [PMID: 38577440 DOI: 10.4251/wjgo.v16.i3.819]

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