{
  "abstract": "Purpose\n\nTo develop HepaKineticNet, a deep learning framework that models inter-phase kinetic dynamics on multi-phase contrast-enhanced CT (CECT) to generate intratumoral heterogeneity (ITH) maps, and to assess in an independent external cohort whether a clinical-radiomics fusion model improves overall survival (OS) prediction in BCLC stage B-C hepatocellular carcinoma (HCC) treated with locoregional therapy plus first-line TKI and anti-PD-1 agents.Patients and methods\n\nIn this bi-institutional retrospective study, HepaKineticNet was developed using 359 patients from one institution (231 training, 128 validation) and evaluated in an independent external test cohort of 95 patients from a second institution. HepaKineticNet integrates a Spatial Kinetic Differential Attention module into an nnU-Net backbone, separately encoding arterial wash-in and delayed wash-out kinetics, with multiscale coefficient-of-variation self-supervision. Ten radiomics features (hierarchical filtering) and three clinical variables selected by LASSO-penalised Cox regression (10-fold cross-validation) were used to build clinical, radiomics, and fusion Cox models.Results\n\nA total of 277 deaths occurred, 128 of them in the development cohort. Baseline characteristics were balanced (all P>0.05). In the external test cohort, the fusion model achieved a C-index of 0.713, exceeding the clinical model (0.651; P=0.012). Time-dependent AUCs at 1, 2, and 3 years were 0.760 (95%CI 0.651-0.864), 0.838 (95%CI 0.736-0.930), and 0.909 (95%CI 0.686-1.000), whereas those of the clinical model were 0.676, 0.742, and 0.772. Kaplan-Meier analysis showed median OS of 9.5 vs 19.0 months for high- vs low-risk strata (HR 3.02, 95%CI 1.86-4.91; P<0.001). Discrimination was consistent across ten prespecified subgroups.Conclusion\n\nHepaKineticNet-derived ITH maps encode kinetic and spatial information that modestly improves individualized OS prediction and risk stratification in this combination-therapy HCC population, and may provide a noninvasive decision-support adjunct pending prospective validation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Interventional Therapy I, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, People's Republic of China.",
    "Department of Oncology, Yantai Qishan Hospital, Yantai Public Health Clinical Center, Yantai, 264001, People's Republic of China.",
    "Interventional Operating Room, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, People's Republic of China.",
    "Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 265400, People's Republic of China.",
    "School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, 271016, People's Republic of China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinpeng Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Wenzhe Bu"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaotian Hanming"
    },
    {
      "affs": [
        3
      ],
      "name": "Qianqian Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinlong Song"
    },
    {
      "affs": [
        4
      ],
      "name": "Wei Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bgvnprtx9x384.bib",
  "cited_by": 0,
  "doi": "10.2147/jhc.s636207",
  "fulltext_url": "https://europepmc.org/articles/PMC13637505",
  "id": "bgvnprtx9x384",
  "issns": [
    "2253-5969"
  ],
  "landing_url": "https://europepmc.org/article/MED/42836110",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7215074153",
  "pdf_url": "https://europepmc.org/articles/PMC13637505?pdf=render",
  "pmcid": "PMC13637505",
  "pmid": "42836110",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bgvnprtx9x384.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "MED:42836110",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42836110"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "HepaKineticNet: Deep Learning of CT-Derived Intratumoral Heterogeneity for Overall Survival Prediction in BCLC Stage B-C Hepatocellular Carcinoma Treated with Locoregional Therapy Plus First-Line TKI and Anti-PD-1 Therapy.",
  "title_text": "HepaKineticNet: Deep Learning of CT-Derived Intratumoral Heterogeneity for Overall Survival Prediction in BCLC Stage B-C Hepatocellular Carcinoma Treated with Locoregional Therapy Plus First-Line TKI and Anti-PD-1 Therapy.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:10Z",
  "url": "https://research.draconitic.com/paper/bgvnprtx9x384",
  "venue": "Journal of hepatocellular carcinoma",
  "versions": []
}