{
  "abstract": "Cerebral tuberculomas and brain metastases of bronchial carcinoma are often indistinguishable on magnetic resonance imaging (MRI), leading to misdiagnosis. This study develops a contrast-enhanced MRI radiomics-based nomogram to accurately differentiate them. A total of 482 patients (290 males, 192 females; mean age 56.08±12.96 years), including 254 with cerebral tuberculomas and 228 with brain metastases of bronchial carcinoma were enrolled from two centers. Radiomic features were extracted, standardized, and reduced via significance testing, Pearson correlation, and least absolute shrinkage and selection operator (LASSO) regression. A radiomic model was built, then integrated with clinical predictors into a nomogram. Performance was assessed across training, internal validation, and external validation cohorts using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). A total of 13 radiomic features were selected for model development. The radiomic model demonstrated areas under the curve (AUCs) of 0.907, 0.891 and 0.886 in training, internal validation, and independent external validation cohorts, respectively, significantly outperforming models based solely on clinical factors, which achieved AUCs of 0.781, 0.712, and 0.828 (P<0.05). The nomogram, which integrated the radiomic scores (Rad-scores), age, ring enhancement, meningeal thickening, and carcinoembryonic antigen (CEA) levels, exhibited superior performance with AUCs of 0.951, 0.937, and 0.940 across the three cohorts. DCA demonstrated that the nomogram provided greater clinical net benefits across a range of threshold probabilities, and calibration curves indicated strong consistency between predicted probabilities and actual outcomes. A nomogram combining clinical factors and contrast-enhanced MRI radiomics accurately differentiates cerebral tuberculomas from brain metastases of bronchial carcinoma, offering robust decision support for individualized diagnosis and treatment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China.",
    "Department of Radiology, Hunan Institute for Tuberculosis Control, Hunan Chest Hospital, Changsha, China.",
    "Department of Internal Medicine, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China.",
    "Institute of Pulmonary Diseases, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ruiyun Liang"
    },
    {
      "affs": [
        1
      ],
      "name": "Quhua Yin"
    },
    {
      "affs": [
        2
      ],
      "name": "Chengcheng Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Huili Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "Huiru Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Min Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Hui Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingqiang Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Weijun Fang"
    },
    {
      "affs": [
        3
      ],
      "name": "Qi Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/87vp9byjxbkbz.bib",
  "cited_by": 0,
  "doi": "10.21037/qims-2026-1518",
  "fulltext_url": "https://europepmc.org/articles/PMC13743333",
  "id": "87vp9byjxbkbz",
  "issns": [
    "2223-4292",
    "2223-4306"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997944",
  "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": "W7222712353",
  "pdf_url": "https://europepmc.org/articles/PMC13743333?pdf=render",
  "pmcid": "PMC13743333",
  "pmid": "42997944",
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/87vp9byjxbkbz.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:41Z",
      "native_id": "MED:42997944",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997944"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Development of a nomogram integrating contrast-enhanced magnetic resonance imaging radiomics and clinical parameters for differentiating brain metastases of bronchial carcinoma from cerebral tuberculomas.",
  "title_text": "Development of a nomogram integrating contrast-enhanced magnetic resonance imaging radiomics and clinical parameters for differentiating brain metastases of bronchial carcinoma from cerebral tuberculomas.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:41Z",
  "url": "https://research.draconitic.com/paper/87vp9byjxbkbz",
  "venue": "Quantitative imaging in medicine and surgery",
  "versions": []
}