{
  "abstract": "Bone metastasis is a common and clinically important complication of non-small cell lung cancer (NSCLC) and is associated with poor survival. Although positron emission tomography-computed tomography (PET-CT) provides comprehensive skeletal assessment, its cost and radiation exposure may limit routine use. Therefore, an accessible approach based on routinely available clinical and chest CT-related information may help identify patients at higher risk who could benefit from further skeletal imaging. This study aimed to develop an effective predictive model for bone metastasis in patients with NSCLC. This study retrospectively analyzed data from 17,646 Asian NSCLC patients in the Surveillance, Epidemiology, and End Results (SEER) database (2000-2021) as a training set and used an independent external cohort of 308 NSCLC patients from Shanghai Chest Hospital (2022-2023) for validation. Univariate and multivariate logistic regression (LR) analyses and Collinearity analysis were performed for variable selection. A novel DualDrop-Gated Transformer (DGT) model was developed and compared with LR, extreme gradient boosting (XGBoost), LightGBM, TabTransformer, and FTTransformer. Performance was assessed by receiver operating characteristic (ROC), calibration, and decision curves. Age, sex, T stage, N stage, histological subtype, and lung metastasis status were identified as significant predicting variables. The DGT model achieved an area under the curve (AUC) of 0.947 in the training set, 0.936 in the internal test set, and 0.879 in the external validation cohort with balanced sensitivity (0.856) and specificity (0.934), significantly outperforming other models (P<0.01). Ablation experiments demonstrated the significant contributions of gating mechanisms and dropout to model performance. A nomogram based on the DGT model was constructed, with reasonable calibration, and decision curve analysis (DCA) revealed good clinical utility. A novel DGT prediction model for bone metastasis in patients with NSCLC based on a highly generalizable and interpretable deep learning framework was developed and validated using 6 predictors, which may have potential application value in early screening, decision-making, and individualized management for lung cancer patients.",
  "abstract_shown": true,
  "affiliations": [
    "School of Medical Instrument, Shanghai University of Medicine & Health Sciences, Shanghai, China.",
    "School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.",
    "Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.",
    "Cardio-Thoracic Surgery, Xinjiang Medical University, Urumqi, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiaotong Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Hong Yu"
    },
    {
      "affs": [
        2
      ],
      "name": "Yan Shen"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Qian Qian"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaokun Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Ziming Xie"
    },
    {
      "affs": [
        2
      ],
      "name": "Junfeng Gu"
    },
    {
      "affs": [
        2
      ],
      "name": "Guangyu Tao"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhenqi Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "ShaoJie Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Jianhua Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Kai Nie"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Liping Sun"
    },
    {
      "affs": [
        2
      ],
      "name": "Lin Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/azhq06wpzjxag.bib",
  "cited_by": 0,
  "doi": "10.21037/qims-2026-0574",
  "fulltext_url": "https://europepmc.org/articles/PMC13743291",
  "id": "azhq06wpzjxag",
  "issns": [
    "2223-4292",
    "2223-4306"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997782",
  "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": "W7223228194",
  "pdf_url": "https://europepmc.org/articles/PMC13743291?pdf=render",
  "pmcid": "PMC13743291",
  "pmid": "42997782",
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/azhq06wpzjxag.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:41Z",
      "native_id": "MED:42997782",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997782"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "A DualDrop-Gated Transformer model for predicting bone metastasis in Asian patients with non-small cell lung cancer: a real-world study with external validation.",
  "title_text": "A DualDrop-Gated Transformer model for predicting bone metastasis in Asian patients with non-small cell lung cancer: a real-world study with external validation.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:41Z",
  "url": "https://research.draconitic.com/paper/azhq06wpzjxag",
  "venue": "Quantitative imaging in medicine and surgery",
  "versions": []
}