{
  "abstract": "Reliable pretreatment tools to identify treatment-naïve patients with intermediate-to-advanced hepatocellular carcinoma (HCC) who may benefit from this combination are lacking. We developed and internally validated a clinical-radiomics model based on pretreatment T2-weighted fat-suppressed magnetic resonance imaging (MRI) for predicting 1-year overall survival (OS). This retrospective, single-center cohort study included 85 treatment-naïve patients with Barcelona Clinic Liver Cancer (BCLC) stage B/C HCC treated with transarterial chemoembolization (TACE) plus targeted therapy and immune checkpoint inhibitors between December 2021 and April 2024. The last follow-up was April 1, 2026. The primary endpoint was 1-year OS status (death within 12 months vs. survival ≥12 months). Patients were randomly divided into a training set (70%) and a held-out test set (30%) using stratified sampling. Three-dimensional tumor volumes were manually segmented on pretreatment axial T2 spectral attenuated inversion recovery images. Features were first filtered for intra- and interobserver reproducibility, then selected using univariable screening and least absolute shrinkage and selection operator (LASSO) regression before being entered into the machine-learning models. The optimal model was selected via fivefold cross-validation in the training set and evaluated in a held-out test set through use of discrimination, calibration, decision curve analysis, and Shapley additive explanations. A total of 2,525 radiomics features were extracted, and four final predictors were retained: maximum tumor diameter and three filtered radiomics features. Gradient boosting machine (GBM) achieved the highest mean cross-validation area under the receiver operating characteristic curve (AUC) (0.818±0.079). In the held-out test set, the combined GBM model yielded an AUC of 0.91 [95% confidence interval (CI): 0.87-0.93], accuracy of 84.6% (95% CI: 79.3-88.9%), sensitivity of 81.8% (95% CI: 72.2-89.0%), and specificity of 86.7% (95% CI: 79.1-92.1%); the Brier score was 0.04. Decision curve analysis suggested a net clinical benefit across a broad range of threshold probabilities. A pretreatment T2-weighted fat-suppressed MRI-based clinical-radiomics model demonstrated promising performance for predicting 1-year OS in treatment-naïve patients with BCLC stage B/C HCC receiving TACE plus targeted therapy and immune checkpoint inhibitors. However, this single-center finding requires external validation before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Interventional Radiology, Gaoxin Branch, Jilin Cancer Hospital, Changchun, China.",
    "Department of Radiology, Jilin Cancer Hospital, Changchun, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chengyi Jiang"
    },
    {
      "affs": [
        1
      ],
      "name": "Shuai Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Beiguang Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingguang Xin"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyu Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiale Cheng"
    },
    {
      "affs": [
        1
      ],
      "name": "Dingli Ye"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiqiang Sun"
    },
    {
      "affs": [
        1
      ],
      "name": "Shuangyan Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cns8myazmhfwr.bib",
  "cited_by": 0,
  "doi": "10.21037/jgo-2026-1080",
  "fulltext_url": "https://europepmc.org/articles/PMC13740543",
  "id": "cns8myazmhfwr",
  "issns": [
    "2078-6891",
    "2219-679X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42996585",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7224105187",
  "pdf_url": "https://europepmc.org/articles/PMC13740543?pdf=render",
  "pmcid": "PMC13740543",
  "pmid": "42996585",
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cns8myazmhfwr.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42996585",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42996585"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.",
  "title_text": "Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/cns8myazmhfwr",
  "venue": "Journal of gastrointestinal oncology",
  "versions": []
}