Evidence map›Paper›PMID 42907728›Full record

ArticleJournal of imaging informatics in medicine2026

TRACE-CLDN: An Integrated Computational Pathology Framework for Tumor-Restricted Quantification of CLDN18.2 Expression Burden from H&E Whole-Slide Images in Intrahepatic Cholangiocarcinoma.

Yu-Meng Pan, Hao-Tian Zhang, Meng-Ling Liu, Xin-Yi Jiang, Zhi-Qiang Yang, Li Sui

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Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yu-Meng PanSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Hao-Tian ZhangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Meng-Ling LiuSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Xin-Yi JiangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Zhi-Qiang YangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Li SuiSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. lsui@usst.edu.cn.

Funding

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6 · The paper itself

Abstract

Claudin-18.2 (CLDN18.2) is a membrane-associated biomarker with emerging translational relevance in biliary tract cancers. In intrahepatic cholangiocarcinoma (ICC), whole-slide image (WSI)-level assessment is complicated by abundant stroma, dispersed tumor glands, non-neoplastic epithelial structures, and heterogeneous membranous staining. We developed TRACE-CLDN (Tumor-Restricted Analysis for Computational Estimation of CLDN18.2), an integrated computational pathology framework for tumor-restricted quantification of CLDN18.2 expression burden from hematoxylin and eosin (H&E) WSIs. This retrospective study included 135 ICC cases with paired H&E and CLDN18.2 immunohistochemistry (IHC) WSIs. After cross-modal registration, IHC-derived annotations were mapped to H&E images to generate patch-level training data. TRACE-CLDN integrates two independently trained H&E-based modules for tumor region identification and CLDN18.2 expression label prediction, followed by WSI-level tumor-restricted aggregation. Agreement with pathologist consensus was assessed in a 45-case pathologist-validation subset, and exploratory survival analysis was performed in the full cohort. ROC-AUCs were 0.9968 ± 0.0009 for tumor identification and 0.9633 ± 0.0174 for expression prediction. The TRACE-CLDN-derived Ratio showed strong agreement with pathologist assessment (Spearman ρ = 0.876; weighted κ = 0.767). In exploratory survival analysis, the log-rank P value was 0.010 and the concordance index was 0.668. TRACE-CLDN enables tumor-restricted, continuous assessment of CLDN18.2 expression burden from H&E WSIs, with strong agreement with pathologist assessment and an exploratory association with survival in ICC.

Indexed as

CLDN18.2Deep learningDigital pathologyIntrahepatic cholangiocarcinomaWhole-slide imaging

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.