ArticleJHEP reports : innovation in hepatology2025
A pathomics-integrated multimodal model to evaluate chemoimmunotherapy efficacy in unresectable intrahepatic cholangiocarcinoma.
Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Evolving Landscape of Immune Regulation and Immunotherapy in Cholangiocarcinoma and Biliary Tract Cancer.Cancers · 2026Review
- Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.Breast (Edinburgh, Scotland) · 2026Review
- Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts.NPJ digital medicine · 2025Article
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Authors and funding
16 authors.
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Abstract
Background & Aims: Chemoimmunotherapy has emerged as the first-line therapy for unresectable intrahepatic cholangiocarcinoma (ICC). However, durable clinical responses are observed in less than 30% of patients, necessitating biomarkers of survival benefit. Thus, the aim of this study was to develop and validate a pathomics-based prognostic signature for patients with ICC receiving chemoimmunotherapy. Methods: This multicenter study included patients with ICC with biopsy samples. Pathomics features were extracted from digital H&E-stained images, and the pathomics signature for ICC (PS-ICC) was developed by machine learning (ML). SHapley Additive exPlanations provided algorithmic explanation, and The Cancer Genome Atlas database supported biological interpretation. We explored the potential of PS-ICC as surrogate index compared with radiological response. Results: Based on pretreatment specimens, 189 patients receiving chemoimmunotherapy were included. Univariate Cox analysis demonstrated that the PS-ICC status from a pathomics-driven ML model was associated with overall survival (OS) in patients with unresectable ICC undergoing chemoimmunotherapy (training cohort: hazard ratio (HR) = 0.09, 95% CI, 0.05-0.14, Conclusions: The PS-ICC demonstrates potential as a surrogate endpoint for survival prediction in patients with ICC undergoing chemoimmunotherapy, with biological plausibility evidenced by its tumor microenvironment associations. Prospective trials are warranted to confirm clinical utility. Impact and implications: This study developed and validated a machine learning-based pathomics signature that accurately predicts overall survival in patients with intrahepatic cholangiocarcinoma (ICC) receiving chemoimmunotherapy. The pathomics signature for ICC provides a biologically grounded, pretreatment biomarker to stratify patients for chemoimmunotherapy, potentially reducing overtreatment and guiding personalized strategies. By demonstrating strong correlation with overall survival, this signature could serve as a surrogate endpoint in clinical trials, thereby accelerating drug development. Furthermore, its link to immune pathways could inform novel therapeutic targets in ICC. However, prospective validation is needed for clinical adoption.
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