{
  "abstract": "Background: Dual-layer spectral detector computed tomography (DLCT) generates multiple quantitative parameters, such as CT values on virtual non-contrast (VNC) images, iodine concentration (IC), electron density (ED), effective atomic number (Zeff), and CT values on virtual mono-energetic images (VMI), offering new insights for the diagnosis and evaluation of gastrointestinal stromal tumors (GISTs). This study aimed to develop and validate a multimodal predictive model that integrates dual-phase spectral CT parameters with key clinical-radiological features for preoperative risk stratification in GISTs.\n\nMethods: This retrospective study included 130 patients with pathologically confirmed GISTs, comprising 83 low-risk and 47 high-risk cases. Clinical data, traditional CT features and spectral CT quantitative parameters of electron density (ED), virtual non-contrast (VNC) images and spectral curve slope (λ) in both arterial phase (AP) and venous phase (VP) were collected and compared between the two groups. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. A nomogram was constructed based on the selected features, and model performance was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).\n\nResult: The mean age was significantly lower in the low-risk group (56.6 ± 10.8 years) than in the high-risk group (61.3 ± 12.5 years) (P < 0.05). Tumor maximum diameter, necrosis occurrence rates, enlarged vessels feeding or draining the mass (EVFDM) occurrence rates were significantly higher in the high-risk group than the low-risk group (6.7 cm [4.2-8.6] vs. 1.6 cm [1.1–3.1], 72.3% vs. 12.0%, 63.8% vs. 10.8%, respectively; all P < 0.05). ED, VNC in arterial phase also showed significant differences between low-risk and high-risk groups (ED: 104.3 ± 1.0 vs. 103.5 ± 0.53; VNC: 39.7 ± 8.4 HU vs. 36.3 ± 7.1 HU; all P < 0.05). Based on Lasso regression, five features (tumor size, location, necrosis, EVFDM, and AP-ED) were used to construct the integrated model. The model demonstrated strong diagnostic performance, with an AUC of 0.936, sensitivity of 88.0%, specificity of 91.5%, and accuracy of 89.2%.\n\nConclusion: A multimodal predictive model combining spectral CT–derived electron density with selected clinical and radiological features enables accurate preoperative risk stratification of GISTs, and may assist in guiding individualized management strategies.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, Zhongshan City People’s Hospital, No. 2 Sunwendong Street, Zhongshan, 528403 China",
    "Clinical and Technical Support, Philips HealthCare, Guangzhou, 510220 China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Li-Ping Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiu-Yu Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiao-Min Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Liang-Cai Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Fang-Yun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong Quan"
    },
    {
      "affs": [
        0
      ],
      "name": "Bing-Hang Tang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9kgxc89rtpnmv.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02672-x",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13733191/",
  "id": "9kgxc89rtpnmv",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://doi.org/10.1186/s12880-026-02672-x",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2715|Gastrointestinal Tumor Research and Treatment"
    ]
  ],
  "openalex": "W7201888173",
  "pdf_url": null,
  "pmcid": "PMC13733191",
  "pmid": "42983334",
  "published": "2026-08-07",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/9kgxc89rtpnmv.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:23:02Z",
      "native_id": "PMC13733191",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02672-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Prediction of risk stratification in gastrointestinal stromal tumors using electron density combined with clinical and imaging features from spectral CT",
  "title_text": "Prediction of risk stratification in gastrointestinal stromal tumors using electron density combined with clinical and imaging features from spectral CT",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:23:02Z",
  "url": "https://research.draconitic.com/paper/9kgxc89rtpnmv",
  "venue": "BMC Medical Imaging",
  "versions": []
}