Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence

doi:10.4251/wjgo.v18.i2.114782

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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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World J Gastrointest Oncol. Feb 15, 2026; 18(2): 114782
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.114782

Table 2 Multivariable logistic regression analysis of non-curative resection after endoscopic submucosal dissection for superficial esophageal cancer

Predictor	Estimate	SE	OR (95%CI)	Z	P value
SII	0.55	0.295	1.733 (0.968-3.091)	1.863	0.063
Thickness = 5-7 mm	0.321	0.355	1.379 (0.688-2.790)	0.904	0.366
Thickness > 7 mm	1.12	0.379	3.065 (1.463-6.498)	2.956	0.003
EOM = SM1	0.894	0.459	2.445 (1.043-6.469)	1.947	0.052
EOM > SM1	2.327	0.479	10.252 (4.211-28.176)	4.854	0.0
Esophageal stricture	1.227	0.416	3.411 (1.512-7.760)	2.952	0.003
Multiple lesions	0.98	0.401	2.666 (1.210-5.865)	2.444	0.015
CR = 1/2-3/4	0.513	0.411	1.671 (0.763-3.872)	1.248	0.212
CR > 3/4	1.315	0.421	3.724 (1.669-8.799)	3.121	0.002
PPT	1.062	0.341	2.892 (1.513-5.791)	3.117	0.002

SII: Systemic immune-inflammation index; EOM: Endoscopic ultrasound or magnifying endoscopy with narrow-band imaging; SM1: Superficial layer; CR: Circumferential ratio; PPT: Preoperative pathological type.

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Citation: Luo ZC, Guo HY, Tang X, Chen XR, Zhang CY, Cui YT, Zuo J, Li HR, Hou XM, Chen H, Song SB, Wang XF. Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence. World J Gastrointest Oncol 2026; 18(2): 114782
URL: https://www.wjgnet.com/1948-5204/full/v18/i2/114782.htm
DOI: https://dx.doi.org/10.4251/wjgo.v18.i2.114782

Luo ZC, Guo HY, Tang X, Chen XR, Zhang CY, Cui YT, Zuo J, Li HR, Hou XM, Chen H, Song SB, Wang XF. Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence. World J Gastrointest Oncol 2026; 18(2): 114782 [PMID: 41695917 DOI: 10.4251/wjgo.v18.i2.114782]

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