Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer

doi:10.4251/wjgo.v18.i4.115635

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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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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Apr 15, 2026; 18(4): 115635
Published online Apr 15, 2026. doi: 10.4251/wjgo.v18.i4.115635

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Figure 9 Local model explanation by the SHapley Additive exPlanation method. A and B: Shows examples of correctly predicted lymph node metastasis-negative cases; C and D: Shows examples of correctly predicted lymph node metastasis-positive cases. CA 19-9: Carbohydrate antigen 19-9; CEA: Carcinoembryonic antigen.

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

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 [DOI: 10.4251/wjgo.v18.i4.115635]

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