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 6 Diagnostic performance comparison (prediction model vs physician group) on the independent test set

Comparison item	Prediction model	Physician group	P value
T-staging
Accuracy	0.898	0.819	0.043
AUC (95%CI)	0.907 (0.862-0.954)	0.817 (0.757-0.873)	0.016
N-staging
Accuracy	0.949	0.839	0.038
AUC (95%CI)	0.912 (0.867-0.956)	0.765 (0.680-0.857)	0.001

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

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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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