ReviewWorld journal of clinical oncology2025
Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.
Review in World journal of clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A Prognostic Model Based on Adipokine-Related Genes for Hepatocellular Carcinoma: Assessment of Patient Outcomes and Tumor Microenvironment Characterization.Digestive diseases and sciences · 2026Article
- Article
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- C17orf75 (Njmu-R1) promotes hepatocellular carcinoma progression: a pan-cancer analysis and experimental validation.Frontiers in immunology · 2026Article
- Value of radiomics models in precision diagnosis of dual-phenotype hepatocellular carcinoma and intrahepatic cholangiocarcinoma.World journal of radiology · 2025Article
- Nanomaterial-assisted immunodiagnostic profiling and therapeutic targeting of hepatocellular carcinoma: from molecular biomarkers to clinical applications.Frontiers in immunology · 2025Article
- XAI-BT-EdgeNet: explainable edge-aware deep learning with squeeze-and-excitation for brain tumor detection and prediction.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC), a leading cause of cancer mortality, faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity. Pathomics, an emerging discipline that integrates artificial intelligence (AI) with quantitative pathology image analysis, aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens. This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images. Pathomics integrates deep learning with histopathological features to enable precise tumour classification (
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Registered trials
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.