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 1 Baseline characteristic comparison (development vs temporally independent test cohort), mean ± SD/n (%)

Characteristics	Development cohort (n = 680)	Independent test cohort (n = 170)	Statistic	P value
Age (years)	62.56 ± 10.70	63.24 ± 11.44	0.918	0.359
Sex (male/female)	375/305	95/75	0.030	0.863
BMI (kg/m²)	23.5 ± 3.2	23.8 ± 3.4	t = 1.06	0.291
Ileus			0.803	0.370
Yes	68 (10.0)	21 (12.4)
No	612 (90.0)	149 (87.6)
Tumor location
Colon cancer	425 (62.5)	105 (61.8)	χ² test	0.861
Rectal cancer	255 (37.5)	65 (38.2)
Maximum tumor diameter (cm)	4.2 ± 1.8	4.3 ± 1.7	t-test	0.512
Preoperative CEA (≥ 5 ng/mL)	306 (45.0)	77 (45.3)	0.005	0.945
T-staging
T1	85 (12.5)	22 (12.9)	0.051	0.997
T2	170 (25.0)	43 (25.3)
T3	306 (45.0)	75 (44.1)
T4	119 (17.5)	30 (17.6)
N-staging
N0	374 (55.0)	93 (54.7)	0.020	0.995
N1	204 (30.0)	51 (30.0)
N2	102 (15.0)	26 (15.3)
Pathological differentiation			0.203	0.652
High/moderate differentiation	476 (70.0)	122 (71.8)
Poor differentiation/mucinous adenocarcinoma	204 (30.0)	48 (28.2)
Image quality score
3 points (acceptable)	204 (30.0)	51 (30.0)	0.000	1.000
4 points (good)	340 (50.0)	85 (50.0)
5 points (excellent)	136 (20.0)	34 (20.0)

BMI: Body mass index; CEA: Carcinoembryonic antigen.

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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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