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 3 Ablation experimental results on the independent test set

Model configuration	T-stage AUC	N-stage AUC	T-staging accuracy	N-staging accuracy	Weighted Kappa coefficient
Single-path backbone network	0.852	0.861	0.835	0.871	0.784
Removal of dual-path fusion	0.873	0.879	0.859	0.891	0.812
Removal of cross-attention mechanism	0.881	0.886	0.864	0.902	0.828
Removal of feature pyramid network	0.892	0.898	0.876	0.918	0.846
Complete model (the method proposed herein)	0.907	0.912	0.898	0.949	0.883

AUC: Area under the curve.

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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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