{
  "abstract": "Objective: To construct a human epithelial growth factor receptor 2 (HER-2) status predictive model in breast cancer using chest contrast-enhanced fast kVp switching dual-energy CT (DECT).\n\nMethods: One hundred and fourteen breast cancer patients who underwent preoperative contrast-enhanced DECT scanning and surgery were retrospectively collected and divided into the HER-2 high group (n = 41), the HER-2 low group (n = 51) and the HER-2 negative group (n = 22). Conventional imaging features and DECT quantitative parameters were recorded. Independent predictors were identified by multi-variable logistic regression. Based on the binary classification of HER-2 high group and the combination group of HER-2 low and negative group, imaging feature model, DECT parameter model and combined model were constructed. The predictive performance was analyzed using area under the receiver operating characteristic curves (AUC).\n\nResults: Lobulation, microcalcification, arterial-phase NIC and λHu, venous-phase NCT, NIC, NZeff and λHu showed statistically significant differences among the three groups, while only venous-phase NIC was statistically different during multiple comparisons (all P < 0.05). The maximum tumor diameter, arterial-phase normalized iodine concentration (NIC), slope for Hounsfield unite curve (λHu) and venous-phase normalized CT value, NIC, normalized effective atom number (NZeff) and λHu were higher in the HER-2 high group than in the combined group of Her-2 low and Her-2 negative individuals (all P < 0.05). Lobulation, microcalcification, arterial-phase λHu and venous-phase NZeff were identified as independent predictors of high HER-2 expression. Among all metrics, arterial-phase λHu had the highest AUC of 0.720 (95%CI: 0.623–0.816).The imaging feature model, DECT parameter model and combined model showed AUCs of 0.742 (95%CI: 0.647–0.836), 0.770 (95%CI: 0.685–0.854) and 0.831 (95%CI: 0.759–0.904), respectively. The combined model and its corresponding nomogram exhibit favorable calibration and possess clinical value for risk stratification.\n\nConclusion: The model combining imaging features and DECT parameters demonstrated certain reference value for predicting HER‑2 status in breast cancer, and may provide potential auxiliary support for clinical decision‑making. Further validations should be conducted.\n\nClinical trial number: Not applicable.",
  "abstract_shown": true,
  "affiliations": [
    "The School of Clinical Medicine, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian Province China",
    "Department of Radiology, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian China",
    "Department of Radiology, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, No.201-209 Hubinnan Road, Siming District, Xiamen, Fujian Province 361004 China",
    "CT Research Center, GE Healthcare, Shanghai, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Dan Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaoli Zheng"
    },
    {
      "affs": [
        3
      ],
      "name": "Suping Chen"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Ziying Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Sihui Zhong"
    },
    {
      "affs": [
        2
      ],
      "name": "Liwei Sun"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xin Yue",
      "orcid": "0000-0002-1239-2406"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2rz65wv0qzq1r.bib",
  "cited_by": 0,
  "doi": "10.1186/s12880-026-02646-z",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13729318/",
  "id": "2rz65wv0qzq1r",
  "issns": [
    "1471-2342"
  ],
  "landing_url": "https://doi.org/10.1186/s12880-026-02646-z",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2204|Advanced X-ray and CT Imaging"
    ]
  ],
  "openalex": "W7197002734",
  "pdf_url": null,
  "pmcid": "PMC13729318",
  "pmid": "42977926",
  "published": "2026-08-06",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/2rz65wv0qzq1r.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:22:06Z",
      "native_id": "PMC13729318",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12880-026-02646-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Preoperative assessment of HER-2 status in breast cancer using imaging features and quantitative parameters derived from chest contrast-enhanced fast kVp switching dual-energy computed tomography",
  "title_text": "Preoperative assessment of HER-2 status in breast cancer using imaging features and quantitative parameters derived from chest contrast-enhanced fast kVp switching dual-energy computed tomography",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:06Z",
  "url": "https://research.draconitic.com/paper/2rz65wv0qzq1r",
  "venue": "BMC Medical Imaging",
  "versions": []
}