Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer

doi:10.4240/wjgs.v17.i12.112520

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World Journal of Gastrointestinal Surgery

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1948-9366

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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 Surg. Dec 27, 2025; 17(12): 112520
Published online Dec 27, 2025. doi: 10.4240/wjgs.v17.i12.112520

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Figure 1 Flow chat for study design. LASSO: Least absolute shrinkage and selection operator; NNE: Neural network ensemble; ROC: Receiver operating characteristic; SHAP: SHapley Additive exPlanations.

Citation: Li RY, Zhao ZR, Yu T, Yu JC. Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer. World J Gastrointest Surg 2025; 17(12): 112520
URL: https://www.wjgnet.com/1948-9366/full/v17/i12/112520.htm
DOI: https://dx.doi.org/10.4240/wjgs.v17.i12.112520

Li RY, Zhao ZR, Yu T, Yu JC. Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer. World J Gastrointest Surg 2025; 17(12): 112520 [PMID: 41479733 DOI: 10.4240/wjgs.v17.i12.112520]

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