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

Table 1 Computed tomography protocols of the two centers

Center	Scanner	Field of view (cm)	Image matrix (mm)	Tube voltage (kv)	Tube current (mA)	Pixel size (mm)	Slice thickness (mm)
1	Revolution CT (256-slice)	50	512 × 512	70-140	Auto	0.977	0.625-5
1	Discovery CT750 HD (Gemstone)	50	512 × 512	80/100/120/140	Auto	0.977	0.625-5
2	SOMATOM definition flash (dual-source CT)	50	512 × 512	70-140	Auto	0.977	0.625-5

CT: Computed tomography.

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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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