{
  "abstract": "Accurate prediction of epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma (LUAD) with brain metastases (BMs) is crucial for guiding targeted therapy. However, noninvasive and biologically interpretable tools remain limited. In this multicenter radiogenomic study, we analyzed a total of 1303 BMs from 421 LUAD patients across three institutions. 3435 radiomic features were extracted from T1, T2, and contrast-enhanced T1 sequences. A four-task classification framework was developed to predict EGFR mutation status (EGFR+, 19Del, L858R, or sensitizing mutation) using an adaptive LightGBM-based modeling pipeline. The models achieved excellent performance in the internal cohort (AUCs up to 0.95) and were further validated in 94 lesions with pathologically confirmed EGFR status, reaching an accuracy of 83.0%, sensitivity of 84.7%, and specificity of 80.0%. SHAP and LIME analyses revealed that shape-based radiomic features, particularly original_shape_sphericity, were the most important predictors of EGFR mutational subtypes. Then, we conducted transcriptomic analysis on 38 matched surgical specimens. Radiogenomic correlation revealed that sphericity negatively correlated with RNF125 and SLC37A2. Downstream enrichment analysis identified EGFR-associated features linked to DNA replication, sister chromatid segregation, and ERBB signaling. The study demonstrates that radiogenomic modeling, grounded in interpretable biology, holds promise as a non-invasive, clinical strategy for precision stratification of LUAD BMs.",
  "abstract_shown": true,
  "affiliations": [
    "The department of oncology, Xiangya Hospital, Central South University, Changsha, China",
    "National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China",
    "Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha, China",
    "Bioengineering Department and Imperial-X, Imperial College London, London, UK",
    "National Heart and Lung Institute, Imperial College London, London, UK",
    "Cardiovascular Research Centre, Royal Brompton Hospital, London, UK",
    "School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fuxing Deng"
    },
    {
      "affs": [
        0
      ],
      "name": "Xianjing Chu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wen Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Gang Xiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Guilong Tanzhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Lishui Niu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zijian Zhang",
      "orcid": "0000-0002-2702-8025"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Rongrong Zhou",
      "orcid": "0000-0003-4483-0615"
    },
    {
      "affs": [
        3,
        4,
        5,
        6
      ],
      "name": "Guang Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ctkw9n4ed6c1.bib",
  "cited_by": 2,
  "doi": "10.1038/s41746-026-02931-9",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13720021/",
  "id": "4ctkw9n4ed6c1",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02931-9",
  "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",
      "Article"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7167460839",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02931-9_reference.pdf",
  "pmcid": "PMC13720021",
  "pmid": "42410125",
  "published": "2026-07-06",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/4ctkw9n4ed6c1.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:23:53Z",
      "native_id": "PMC13720021",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-02931-9"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Radiogenomic modeling of EGFR mutation status in brain metastases from lung adenocarcinoma: a multicenter study with biological interpretability",
  "title_text": "Radiogenomic modeling of EGFR mutation status in brain metastases from lung adenocarcinoma: a multicenter study with biological interpretability",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:00Z",
  "url": "https://research.draconitic.com/paper/4ctkw9n4ed6c1",
  "venue": "NPJ Digital Medicine",
  "versions": []
}