ArticlePatterns (New York, N.Y.)2025
A multimodal LLM-agent framework for personalized clinical decision-making in hepatocellular carcinoma.
Article in Patterns (New York, N.Y.), 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
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
7 citing papers in PubMed.
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Tumor capsule status modifies the survival impact of anatomical versus non-anatomical hepatectomy in early-stage hepatocellular carcinoma: a multicenter retrospective study.World journal of surgical oncology · 2026Article
- Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.Scientific reports · 2026Article
- Vision-Language Models in Medical Imaging for Cancer Diagnosis: A Bibliometric Review.Bioengineering (Basel, Switzerland) · 2026Review
- Machine learning models for predicting liver cancer: a real-world cohort study in China.Frontiers in cell and developmental biology · 2026Article
- A multimodal LLM-agent framework for personalized clinical decision-making in hepatocellular carcinoma.Patterns (New York, N.Y.) · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) treatment is challenging due to tumor heterogeneity and patient variability. Current guidelines often overlook individual factors, limiting treatment precision. We developed an integrated framework combining radiomics, deep learning, and large language model (LLM)-based decision agents to generate personalized HCC treatment recommendations. A modified GhostNet incorporating dilated convolutions, channel and spatial attention mechanism (CBAM), and residual channel attention (RCA) modules was trained on MRI to predict pathological markers such as microvascular invasion (MVI), capsule presence, and tumor differentiation. A fusion model integrating radiomics and deep learning enhanced prediction accuracy. Six AI agents processed structured multimodal data and generated individualized treatment strategies, which were evaluated by hepatobiliary surgeons. The fusion model significantly improved prediction accuracy, with MVI and capsule presence reaching 0.8902 and 0.8765, respectively. DeepSeek-R1 achieved the highest clinical relevance score, followed by GPT-4 and Med-PaLM 2. This framework demonstrates the feasibility of AI-assisted, patient-specific HCC decision-making, offering a promising direction for precision oncology.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.