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

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Figure 6 Two case examples illustrating SHapley Additive exPlanations-based interpretation of the logistic regression model. CR: Circumferential ratio; EOM: Endoscopic ultrasound or magnifying endoscopy with narrow-band imaging; PPT: Postoperative pathological type; HGIN/LGIN: High-grade intraepithelial neoplasia/Low-grade intraepithelial neoplasia.

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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