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October 11, 2025Deleted Journal

Noninvasive Multi-Omics Radiomic Model Integrating scRNA-seq and Bulk RNA-seq for Hepatocellular Carcinoma Prognosis

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Authors

YGYuan GaoYMYang MiaoHCHoujian Cai

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Overview

Machine learning enhances HCC risk stratification using radiomics and multi-omics data, implying advanced patient management.

Key Points

  • Integration of multi-omics data enhances the prognostic model's accuracy for hepatocellular carcinoma.
  • Patients with higher tumor microenvironment risk scores had a significantly reduced survival rate (HR: 2.13).
  • A support vector machine model, utilizing selected radiomic features, achieved an AUC of 0.85 for prognosis.
  • This non-invasive radiomic framework may revolutionize patient stratification and management in HCC.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68ea72339f1bd4df558cedb7https://doi.org/10.1007/s10278-025-01668-3
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Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Strategies for discovering novel hepatocellular carcinoma biomarkers2025 · 7 citations
  2. 2Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma2023 · 11 citations
  3. 3Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets2023 · 127 citations