Li JY, Zhao Y. Systematic assessment of mixed imputation methods and explainable machine learning. World J Gastrointest Surg 2026; 18(7): 119087 [DOI: 10.4240/wjgs.v18.i7.119087]
Corresponding Author of This Article
Yan Zhao, MD, PhD, Chief Physician, Director, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, No. 44 Xiaoheyan Street, Dadong District, Shenyang 110042, Liaoning Province, China. drzhao@dlut.edu.cn
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Gastroenterology & Hepatology
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review-article
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Li JY, Zhao Y. Systematic assessment of mixed imputation methods and explainable machine learning. World J Gastrointest Surg 2026; 18(7): 119087 [DOI: 10.4240/wjgs.v18.i7.119087]
World J Gastrointest Surg. Jul 27, 2026; 18(7): 119087 Published online Jul 27, 2026. doi: 10.4240/wjgs.v18.i7.119087
Systematic assessment of mixed imputation methods and explainable machine learning
Jia-Yi Li, Yan Zhao
Jia-Yi Li, Department of Medicine, Dalian University of Technology, Dalian 116024, Liaoning Province, China
Jia-Yi Li, Yan Zhao, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, Shenyang 110042, Liaoning Province, China
Author contributions: Li JY and Zhao Y contributed to this paper; Li JY designed the overall concept and outline of the manuscript; Zhao Y contributed to the discussion and design of the manuscript; Li JY and Zhao Y contributed to the writing, and editing the manuscript and review of literature. All authors have read and approved the final manuscript.
AI contribution statement: Grammarly and DeepL were used for language polishing, grammar correction, and/or translation assistance. No AI tool was used to generate research data, perform data analysis, create references, formulate scientific conclusions, or replace the authors’ intellectual input. All AI-assisted language changes were carefully reviewed, edited, and approved by the authors.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yan Zhao, MD, PhD, Chief Physician, Director, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, No. 44 Xiaoheyan Street, Dadong District, Shenyang 110042, Liaoning Province, China. drzhao@dlut.edu.cn
Received: January 19, 2026 Revised: February 1, 2026 Accepted: April 14, 2026 Published online: July 27, 2026 Processing time: 189 Days and 18.5 Hours
Abstract
The convergence of artificial intelligence and precision oncology is frequently hampered by the quality of real-world clinical data, particularly the pervasive challenge of missing values. This opinion review critically appraises the methodology and evidentiary framework of the study, which proposes a hybrid imputation architecture, HDI-MF-Gower, integrated with an extra trees classifier and Shaply Additive exPlanation interpretability for predicting survival outcomes following curative gastrectomy. We deconstruct the pivotal assumptions and potential sensitivities of their adaptive weighted similarity initialization. This design is engineered to provide a “warm start” aligned with the underlying data structure for iterative imputation, theoretically mitigating the risks of distributional distortion associated with simplistic initialization strategies. However, a primary boundary of the current evidence lies in the validation hierarchy; the reported validation relies predominantly on random splitting within a single-center cohort, lacking the robustness of temporal extrapolation or genuine external validation. Furthermore, statistical comparisons suggest that the performance differences between the proposed model and several robust ensemble baselines are not consistently distinguishable, making it difficult to attribute performance gains solely to the specific choice of the learner. We conclude that future research must construct a more rigorous evidence chain within multicenter and multimodal frameworks. Crucially, adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence guidelines - specifically regarding missing data mechanisms, sensitivity analyses, calibration and net benefit assessments, and the availability of reproducible materials - is essential to substantiate generalizable clinical utility.
Core Tip: This opinion review evaluates the study, which introduces a hybrid imputation framework, HDI-MF-Gower, combined with an explainable extra trees classifier for predicting postoperative survival in gastric cancer. The study’s innovative approach addresses the challenges of missing clinical data by adapting the iterative imputation method MissForest. Although the model demonstrates clinical transparency and methodological rigor, its marginal improvement over existing ensemble methods and lack of external validation highlight areas for further research in multimodal data integration and multi-institutional validation.
