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Zhu JY, Lin H, Zhao JW, Yang ZY. Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis. World J Gastroenterol 2027; 33(1): 123989 [DOI: 10.3748/wjg.123989]
World J Gastroenterol. Jan 7, 2027; 33(1): 123989 Published online Jan 7, 2027. doi: 10.3748/wjg.123989
Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis
Jia-Yun Zhu, Hao Lin, Jing-Wei Zhao, Zi-Yi Yang
Jia-Yun Zhu, Jing-Wei Zhao, Zi-Yi Yang, Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
Jia-Yun Zhu, Jing-Wei Zhao, Zi-Yi Yang, Shanghai Key Laboratory of Biliary Tract Disease Research, Shanghai 200092, China
Jia-Yun Zhu, Jing-Wei Zhao, Zi-Yi Yang, Shanghai Research Center of Biliary Tract Disease, Shanghai 200092, China
Jia-Yun Zhu, Jing-Wei Zhao, Zi-Yi Yang, Biliary Disease Research Institute of Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
Hao Lin, College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 201318, China
Co-first authors: Jia-Yun Zhu and Hao Lin.
Co-corresponding authors: Jing-Wei Zhao and Zi-Yi Yang.
Author contributions: Zhu JY and Lin H collected and organized the data and drafted the manuscript, they contributed equally to this article, they are the co-first authors of this manuscript; Zhao JW and Yang ZY reviewed and revised the manuscript; they contributed equally to this article, they are the co-corresponding authors of this manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
AI contribution statement: The authors used Grammarly and GPT for language polishing and grammar checking during the preparation of this manuscript. All authors have reviewed and edited the content and take full responsibility for the final version of the manuscript.
Supported by the Interdisciplinary Program of Shanghai Jiao Tong University, No. YG2024QNA19.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Corresponding author: Zi-Yi Yang, MD, Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai 200092, China. yangziyi@xinhuamed.com.cn
Received: June 4, 2026 Revised: June 29, 2026 Accepted: September 9, 2026 Published online: January 7, 2027 Processing time: 159 Days and 16.1 Hours
Abstract
BACKGROUND
Early diagnosis of gallbladder cancer (GBC) remains challenging because malignant lesions can mimic benign inflammatory or polypoid gallbladder disease. Artificial intelligence (AI) imaging models have been reported for ultrasound-, computed tomography-, and radiomics-based diagnosis; however, the evidence often conflates clinical validation cohorts with image-level model-development experiments.
AIM
To evaluate the diagnostic test accuracy (DTA) of AI-assisted imaging for detecting GBC and to distinguish patient-level clinical validation evidence from technical benchmark studies.
METHODS
Reporting followed PRISMA 2020 and the PRISMA-DTA extension. Risk of bias and applicability were assessed with a Quality Assessment of Diagnostic Accuracy Studies-2 framework tailored to AI imaging. The primary synthesis used a Reitsma bivariate random-effects model. Robustness was examined using a generalized linear mixed model following the meta-analysis of DTA implementation, leave-one-out analyses, exclusion of internal validation, modality/model subgroup analyses, and Hartung-Knapp random-effects diagnostic odds ratio analysis.
RESULTS
Of 485 records, 32 studies met the inclusion criteria and five patient-level validation datasets suitable for 2 × 2 reconstruction, including four independent, external, or prospective validation settings and one internal non-training validation cohort, comprising 615 patients/cases, were meta-analyzed. In these five validation datasets, AI-assisted imaging was associated with pooled sensitivity of 0.888 (95% confidence interval: 0.834-0.927) and moderate-to-high specificity of 0.838 (95% confidence interval: 0.717-0.914), with a summary receiver operating characteristic area under the curve of 0.921. Prediction intervals were wider for specificity than sensitivity (0.529-0.959 vs 0.785-0.946), indicating less stable false-positive performance across settings. Because only five datasets were available, these estimates should be interpreted as preliminary and context-dependent.
CONCLUSION
AI-assisted imaging showed promising sensitivity and moderate-to-high specificity for GBC detection in a small set of patient-level validation datasets. However, the quantitative evidence rested on only five datasets, included heterogeneous modalities, target contrasts, models, thresholds, and validation designs, and showed greater uncertainty for specificity than sensitivity. Current evidence is most consistent with AI as a triage or second-reader adjunct requiring further external and prospective validation, not as a stand-alone diagnostic replacement.
Core Tip: Early diagnosis of gallbladder cancer remains challenging because malignant lesions often mimic benign gallbladder diseases. This study provides the first diagnostic test accuracy meta-analysis focused exclusively on patient-level validation evidence for artificial intelligence-based imaging diagnosis of gallbladder cancer. By separating clinical validation cohorts from image-level development studies, we found that only five datasets were suitable for quantitative synthesis despite 32 eligible publications. Artificial intelligence-assisted imaging achieved high diagnostic performance and may serve as an effective adjunct for lesion triage and radiological decision support, although robust prospective multicenter validation is still required before routine clinical implementation.
