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

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Figure 2 Calibration plot of the integrated multimodal model in the validation cohort (n = 56). Calibration plot comparing predicted vs observed probabilities of advanced neoplasia across deciles of risk. The diagonal line represents perfect calibration. The model demonstrated good agreement between predicted and observed risk (calibration intercept 0.06; slope 0.95; Brier score 0.11), indicating stable probability estimation in internal validation.

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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