ArticleJournal for immunotherapy of cancer2025
Preliminary qualification of a machine learning-based assessment of the tumor immune infiltrate as a predictor of outcome in patients with hepatocellular carcinoma treated with atezolizumab plus bevacizumab.
Article in Journal for immunotherapy of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Review
- Elevated TARDBP expression correlates with an unfavorable prognosis, immune evasion, and poor immune efficacy in hepatocellular carcinoma.Translational cancer research · 2026Article
- Mechanisms and therapeutic strategies of bidirectional crosstalk between hepatic stellate cell-derived cancer-associated fibroblasts and T cells in immune evasion and therapeutic resistance of hepatocellular carcinoma.Frontiers in immunology · 2026Review
- Tissue and blood-based predictive biomarkers in hepatocellular carcinoma immunotherapy: synthesis of 2023-2026 evidence and a proposed clinical integration framework.Frontiers in oncology · 2026Review
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
41 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spontaneously immunogenic hepatocellular carcinoma (HCC), identified by a dense immune cell infiltrate (ICI), responds better to immunotherapy, although no validated biomarker exists to identify these cases. We used machine learning (ML) to quantify ICI from standard H&E-stained tissue and evaluated its correlation with characteristics of the tumor microenvironment (TME) and clinical outcome from atezolizumab plus bevacizumab (A+B).We therefore employed a supervised ML algorithm on 102 pretreatment H&E slides collected from patients treated with A+B. We quantified tumor, stroma and immune cell counts/mm
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