Development and clinical application of an ultrasound-based deep learning model for preoperative staging of colorectal cancer

doi:10.4251/wjgo.120437

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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. Jul 15, 2026; 18(7): 120437
Published online Jul 15, 2026. doi: 10.4251/wjgo.120437

Table 2 Diagnostic performance of the prediction model for the development and independent test cohorts

		AUC (95%CI)	Sensitivity	Specificity	Accuracy	Weighted Kappa (95%CI)
T staging
T1 vs T2-4	Development cohort	0.868 (0.747-0.988)	0.769	0.966	0.941
T1 vs T2-4	Independent test cohort	0.898 (0.809-0.959)	0.864	0.932	0.925
T2 vs others	Development cohort	0.934 (0.867-1.000)	0.880	0.987	0.961
T2 vs others	Independent test cohort	0.899 (0.832-0.952)	0.860	0.937	0.917
T3 vs others	Development cohort	0.958 (0.918-0.999)	0.935	0.982	0.961
T3 vs others	Independent test cohort	0.896 (0.855-0.946)	0.813	0.979	0.910
T4 vs others	Development cohort	0.927 (0.849-1.000)	0.889	0.964	0.951
T4 vs others	Independent test cohort	0.949 (0.888-0.982)	0.933	0.964	0.959
Total	Development cohort	0.888 (0.802-0.974)
	Development cohort	0.888 (0.802-0.974)				0.862 (0.821-0.903)
	Independent test cohort	0.907 (0.862-0.954)				0.841 (0.792-0.890)
N staging
N0 vs others	Development cohort	0.868 (0.801-0.935)	0.911	0.826	0.873
N0 vs others	Independent test cohort	0.917 (0.807-1.000)	1.000	0.833	0.923
N1 vs others	Development cohort	0.847 (0.767-0.927)	0.806	0.887	0.863
N1 vs others	Independent test cohort	0.915 (0.850-0.980)	0.830	1.000	0.946
N2 vs others	Development cohort	0.955 (0.888-1.000)	0.933	0.977	0.971
N2 vs others	Independent test cohort	0.900 (0.816-0.984)	0.800	1.000	0.966
Total	Development cohort	0.874 (0.828-0.920)
	Development cohort	0.874 (0.828-0.920)				0.874 (0.835-0.913)
	Independent test cohort	0.912 (0.867-0.956)				0.858 (0.811-0.905)

AUC: Area under the curve; CI: Confidence interval.

Full Size Table

Citation: Zhao J, Du LJ, Liu Y, Zhu DD, Wang HQ, Shen MK, Wang LY, Wang HY. Development and clinical application of an ultrasound-based deep learning model for preoperative staging of colorectal cancer. World J Gastrointest Oncol 2026; 18(7): 120437
URL: https://www.wjgnet.com/1948-5204/full/v18/i7/120437.htm
DOI: https://dx.doi.org/10.4251/wjgo.120437

Zhao J, Du LJ, Liu Y, Zhu DD, Wang HQ, Shen MK, Wang LY, Wang HY. Development and clinical application of an ultrasound-based deep learning model for preoperative staging of colorectal cancer. World J Gastrointest Oncol 2026; 18(7): 120437 [DOI: 10.4251/wjgo.120437]

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