Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma

doi:10.4251/wjgo.v18.i6.117851

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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

Retrospective Study

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. Jun 15, 2026; 18(6): 117851
Published online Jun 15, 2026. doi: 10.4251/wjgo.v18.i6.117851

Table 1 Clinical characteristic of esophageal patients, median (interquartile range)/mean (SD)

Variables	Whole cohort (n = 206)	Training cohort (n = 144)	Validation cohort (n = 62)	P value
Age	64.1 (7.5)	64.5 (7.5)	63.1 (7.4)	0.218
Gender				0.570
Female	45 (21.8%)	33 (22.9%)	12 (19.4%)
Male	161 (78.2%)	111 (77.1%)	50 (80.6%)
Body mass index	23.1 (2.65)	23.1 (2.7)	23.1 (2.5)	0.965
Smoking and drinking				0.267
None	86 (41.7%)	60 (41.7%)	26 (41.9%)
Smoking alone	40 (19.4%)	29 (20.1%)	11 (17.7%)
Drinking alone	13 (6.3%)	6 (4.2%)	7 (11.3%)
Both	67 (32.5%)	49 (34%)	18 (29%)
Squamous cell carcinoma level	1 (0.6, 1.4)	1 (0.6, 1.4)	1.1 (0.8, 1.3)	0.520
Cytokeratin 19 fragment antigen 21-1 level	1.4 (0.9, 1.8)	1.4 (0.9, 1.9)	1.3 (0.8, 1.8)	0.290
Carcinoembryonic antigen level	3.4 (2.6, 4.3)	3.4 (2.6, 4.4)	3.5 (2.6, 4.2)	0.924
Carbohydrate antigen 199	22.3 (14.9, 29.7)	22.5 (14.8, 30)	22.2 (16.8, 27.2)	0.828
Tumor location				0.480
Upper	63 (30.6%)	44 (30.6%)	19 (30.6%)
Middle	130 (63.1%)	89 (61.8%)	41 (66.1%)
Lower	13 (6.3%)	11 (7.6%)	2 (3.2%)
Tumor length	3 (2, 4.5)	3.4 (2, 4.5)	3 (2, 4)	0.082
Computed tomography-reported nodal status				0.407
Negative	148 (71.8%)	101 (70.1%)	47 (75.8%)
Positive	58 (28.2%)	43 (29.9%)	15 (24.2%)
Tumor grade				0.057
G1	16 (7.8%)	7 (4.9%)	9 (14.5%)
G2	150 (72.8%)	109 (75.7%)	41 (66.1%)
G3	40 (19.4%)	28 (19.4%)	12 (19.4%)
Depth of tumor invasion				0.912
cT1	47 (22.8%)	33 (22.9%)	14 (22.6%)
cT2	58 (28.2%)	40 (27.8%)	18 (29%)
cT3	91 (44.2%)	63 (43.8%)	28 (45.2%)
cT4	10 (4.9%)	8 (5.6%)	2 (3.2%)
Node stage				0.506
pN0	127 (61.7%)	89 (61.8%)	38 (61.3%)
pN1	46 (22.3%)	35 (24.3%)	11 (17.7%)
pN2	19 (9.2%)	12 (8.3%)	7 (11.3%)
pN3	14 (6.8%)	8 (5.6%)	6 (9.7%)
Lymph node metastasis status				0.944
Negative	127 (61.7%)	89 (61.8%)	38 (61.3%)
Positive	79 (38.3%)	55 (38.2%)	24 (38.7%)

Full Size Table

Citation: Gu RT, Li X, Cheng W, Wang XW, Jin H, Liu T. Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma. World J Gastrointest Oncol 2026; 18(6): 117851
URL: https://www.wjgnet.com/1948-5204/full/v18/i6/117851.htm
DOI: https://dx.doi.org/10.4251/wjgo.v18.i6.117851

Gu RT, Li X, Cheng W, Wang XW, Jin H, Liu T. Machine-learning models integrating preoperative clinical factors and circulating tumor DNA features predict lymph node metastasis in esophageal carcinoma. World J Gastrointest Oncol 2026; 18(6): 117851 [DOI: 10.4251/wjgo.v18.i6.117851]

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