Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions

doi:10.4251/wjgo.v18.i7.119847

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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 Cohort 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. Jul 15, 2026; 18(7): 119847
Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.119847

Table 3 Counterfactual decision table at representative risk thresholds (validation cohort, n = 56)

Risk threshold	Surgical candidates (n)	Advanced neoplasia correctly classified	Patients without advanced neoplasia potentially spared surgery	Advanced neoplasia missed (n)
20%	20	14/17	33/39	3
30%	16	13/17	36/39	4
40%	13	12/17	37/39	5

Validation distribution: 17 advanced neoplasia, 39 non-advanced. All patients in the cohort underwent resection; therefore, the number of surgeries potentially avoided represents counterfactual estimates based on model classification rather than observed clinical avoidance of surgery.

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Citation: Özden Y, Yüzügülen Ö, Omurca F. Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions. World J Gastrointest Oncol 2026; 18(7): 119847
URL: https://www.wjgnet.com/1948-5204/full/v18/i7/119847.htm
DOI: https://dx.doi.org/10.4251/wjgo.v18.i7.119847

Özden Y, Yüzügülen Ö, Omurca F. Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions. World J Gastrointest Oncol 2026; 18(7): 119847 [DOI: 10.4251/wjgo.v18.i7.119847]

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