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 2 Performances of computed tomography to evaluate lymph node metastasis status in patients with esophageal cancer

Statistic	Value (95%CI)
Statistic	Whole cohort (n = 206)	Training set (n = 144)	Validation set (n = 62)
Overall lymph node status evaluation
Sensitivity	42.11% (30.86%-53.98%)	42.59% (29.23%-56.79%)	40.91% (20.71%-63.65%)
Specificity	80% (72.08%-86.50%)	77.78% (67.79%-85.87%)	85% (70.16%-94.29%)
PPV	55.17% (44.38%-65.50%)	53.49% (41.21%-65.36%)	60% (38.06%-78.55%)
NPV	70.27% (65.70%-74.47%)	69.31% (63.64%-74.45%)	72.34% (64.34%-79.13%)
Accuracy	66.02% (59.11%-72.46%)	64.58% (56.19%-72.37%)	69.35% (56.35%-80.44%)
Thoracic lymph node status evaluation
Sensitivity	47.92% (33.29%-62.81%)	51.35% (34.40%-68.08%)	36.36% (10.93%-69.21%)
Specificity	86.08% (79.68%-91.06%)	85.05% (76.86%-91.20%)	88.24% (76.13%-95.56%)
PPV	51.11% (39.11%-62.99%)	54.29% (40.66%-67.30%)	40% (18.39%-66.35%)
NPV	84.47% (80.46%-87.79%)	83.49% (78.25%-87.66%)	86.54% (80.26%-91.04%)
Accuracy	77.18% (70.84%-82.73%)	76.39% (68.60%-83.06%)	79.03% (66.82%-88.34%)
Abdominal lymph node status evaluation
Sensitivity	28.89% (16.37%-44.31%)	26.67% (12.28%-45.89%)	33.33% (11.82%-61.62%)
Specificity	95.03% (90.44%-97.83%)	93.86% (87.76%-97.50%)	97.87% (88.71%-99.95%)
PPV	61.9% (41.80%-78.62%)	53.33% (31.05%-74.36%)	83.33% (38.76%-97.53%)
NPV	82.7% (79.82%-85.25%)	82.95% (79.59%-85.85%)	82.14% (76.24%-86.83%)
Accuracy	80.58% (74.51%-85.75%)	79.86% (72.37%-86.08%)	82.26% (70.47%-90.80%)

NPV: Negative predictive value; PPV: Positive predictive value.

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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