Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications
Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.
Authors
- Xiao Liang (ORCID: https://orcid.org/0000-0002-9423-9201)
- Tianhan Zhou (ORCID: https://orcid.org/0000-0003-0292-3346)
- Wenbin Ji (ORCID: https://orcid.org/0000-0002-1609-8925)
- Xiaojun Guan (ORCID: https://orcid.org/0000-0003-2313-4477)
- Danwei Du
- Fang Wang (ORCID: https://orcid.org/0000-0001-8979-1520)
- Ziwei Niu (ORCID: https://orcid.org/0000-0003-0171-5158)
- Junhao Zheng (ORCID: https://orcid.org/0000-0002-3688-1239)
- Shihao Li (ORCID: https://orcid.org/0000-0001-6399-4039)
- Hongjie Hu (ORCID: https://orcid.org/0000-0002-9859-5860)
- Jingwei Wei (ORCID: https://orcid.org/0000-0001-5067-6058)
- Liye Tao
- Wei Zhou (ORCID: https://orcid.org/0000-0003-2434-4644)
- Lanfen Lin
- Jie Tian (ORCID: https://orcid.org/0000-0003-0498-0432)
- Liu Yang (ORCID: https://orcid.org/0000-0001-7587-312X)
- Hongjun Chen (ORCID: https://orcid.org/0000-0002-0936-2599)
- Zhengao Xu
- Junjie Xu (ORCID: https://orcid.org/0000-0001-6507-6442)
- Xue Song (ORCID: https://orcid.org/0000-0003-0324-277X)
- Yonggang Xie
- Yali Wang (ORCID: https://orcid.org/0000-0003-2258-584X)
- Zi-Fang Shen (ORCID: https://orcid.org/0000-0003-0916-7555)
- Haoyu Pan
- Yunjun Yang
Institutions
- Zhejiang Chinese Medical University (CN)
- Xidian University (CN)
- Zhejiang University of Science and Technology (CN)
- Shaoxing University (CN)
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Sir Run Run Shaw Hospital (CN)
- Shandong Institute of Automation (CN)
- Zhejiang Cancer Hospital (CN)
- Huzhou Central Hospital (CN)
- Beijing National Laboratory for Molecular Sciences (CN)
- Precision for Medicine (United States) (US)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Zhejiang Lab (CN)
- Wuhan Union Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Zhejiang Taizhou Hospital (CN)
- Union Hospital (CN)
- Beijing Advanced Sciences and Innovation Center (CN)
- Hangzhou Medical College (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
- Taizhou University (CN)
- Beihang University (CN)
Publication Details
- Journal
- Science Advances
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1126/sciadv.ads8323
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 14.96