Deng T, Feng YM, Li CM, Feng JB. Letter to the Editor: From data integration to precision intelligence: The morphological-functional integration pathway of the hepatic alveolar echinococcosis surgical decision system. World J Gastroenterol 2026; 32(28): 118412 [DOI: 10.3748/wjg.118412]
Corresponding Author of This Article
Jun-Bang Feng, MD, Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing 400017, China. junbangfeng@cqu.edu.cn
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Gastroenterology & Hepatology
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letter
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Deng T, Feng YM, Li CM, Feng JB. Letter to the Editor: From data integration to precision intelligence: The morphological-functional integration pathway of the hepatic alveolar echinococcosis surgical decision system. World J Gastroenterol 2026; 32(28): 118412 [DOI: 10.3748/wjg.118412]
World J Gastroenterol. Jul 28, 2026; 32(28): 118412 Published online Jul 28, 2026. doi: 10.3748/wjg.118412
Letter to the Editor: From data integration to precision intelligence: The morphological-functional integration pathway of the hepatic alveolar echinococcosis surgical decision system
Tie Deng, Yan-Mei Feng, Chuan-Ming Li, Jun-Bang Feng
Tie Deng, Chuan-Ming Li, Jun-Bang Feng, Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing 400014, China
Yan-Mei Feng, Department of Respiratory Medicine, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing 400014, China
Co-first authors: Tie Deng and Yan-Mei Feng.
Co-corresponding authors: Chuan-Ming Li and Jun-Bang Feng.
Author contributions: Deng T and Feng YM contribute equally to this study as co-first authors; Li CM and Feng JB contribute equally to this study as co-corresponding authors; Deng T and Feng YM conceived the study and drafted the manuscript; Li CM and Feng JB provided critical revisions and final approval of the article; all authors have read and approved the final manuscript.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript.
Corresponding author: Jun-Bang Feng, MD, Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing 400017, China. junbangfeng@cqu.edu.cn
Received: January 4, 2026 Revised: February 4, 2026 Accepted: February 12, 2026 Published online: July 28, 2026 Processing time: 189 Days and 18.1 Hours
Abstract
A recent study published in the World Journal of Gastroenterology by Zhu et al demonstrates that machine learning can optimize surgical decision-making for hepatic alveolar echinococcosis (HAE). Through SHapley Additive exPlanations analysis, the study indicates that the type of vascular invasion is a key determinant in selecting between hepatectomy and ex vivo liver resection and auto-transplantation. The model developed by Zhu et al offers a novel approach for precise preoperative assessment of HAE, holding significant clinical value. However, the model relies on the number of involved vessels to assess vascular invasion and uses a single-line measurement for lesion size, which may underestimate surgical risks and introduce subjective bias. Comprehensive preoperative assessment requires integrating multiple parameters, including the depth and extent of vascular involvement, three-dimensional lesion volume, and postoperative residual functional liver volume, all of which directly influence surgical planning and prognosis. Future efforts should focus on integrating multimodal data and leveraging machine learning to develop more comprehensive and objective risk prediction systems. Building upon this foundation, this paper proposes establishing a “morphology-function” integrated preoperative assessment paradigm to advance the precision of surgical decision-making in HAE.
Core Tip: Research by Zhu et al has laid the foundation for applying machine learning to preoperative assessment of hepatic alveolar echinococcosis. However, current models exhibit limited performance improvement due to insufficient variables and data. Future efforts should integrate “morphological” and “functional” information to develop more reliable next-generation predictive models.
