ReviewTechnology in cancer research & treatment
An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.
Review in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Machine learning-based models for tumor mutation burden prediction in gastrointestinal cancers: a systematic review and meta-analysis.Discover oncology · 2026Review
- Can generative artificial intelligence enhance evidence-based and personalized medicine?PLoS medicine · 2026Article
- Editorial: Advancing cancer imaging technologies: bridging the gap from research to clinical practice.Frontiers in oncology · 2026Article
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015-2025).Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of digital health technologies, open-access data, and artificial intelligence (AI) is reshaping oncology by enabling more precise and personalized care. This review provides a focused update on AI, radiomics, and data integration in the context of liver oncology, with hepatocellular carcinoma (HCC) and colorectal liver metastases (CRLM) serving as key case models. Through multimodal datasets-including imaging, molecular profiles, and clinical records-AI and machine learning (ML) have demonstrated significant potential in improving early detection, risk stratification, and treatment response prediction in hepatic malignancies. Radiomics-driven tools have enabled non-invasive assessment of tumor biology, microvascular invasion, and therapeutic outcomes, particularly in HCC and CRLM. While applications in breast, lung, and non-metastatic colorectal cancers are briefly referenced for comparison, the central emphasis is on liver tumors as a representative field where AI-enabled precision oncology is rapidly advancing. Practical and ethical challenges surrounding clinical integration are also discussed, positioning liver oncology as a translational model for broader innovation in cancer care.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.