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.
4 affiliations
- 1School of Medical Instrument, Shanghai University of Medicine & Health Sciences, Shanghai, China.
- 2School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- 3Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- 4Cardio-Thoracic Surgery, Xinjiang Medical University, Urumqi, China.
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.
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BibTeX
@article{wang2026dualdropgated,
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.}},
author = {Xiaotong Wang and Hong Yu and Yan Shen and Qian Qian and Xiaokun Gao and Ziming Xie and Junfeng Gu and Guangyu Tao and Zhenqi Zhang and ShaoJie Li and Jianhua Li and Kai Nie and Liping Sun and Lin Zhu},
journal = {Quantitative imaging in medicine and surgery},
year = {2026},
month = sep,
doi = {10.21037/qims-2026-0574},
url = {https://europepmc.org/article/MED/42997782},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - 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.
AU - Xiaotong Wang
AU - Hong Yu
AU - Yan Shen
AU - Qian Qian
AU - Xiaokun Gao
AU - Ziming Xie
AU - Junfeng Gu
AU - Guangyu Tao
AU - Zhenqi Zhang
AU - ShaoJie Li
AU - Jianhua Li
AU - Kai Nie
AU - Liping Sun
AU - Lin Zhu
PY - 2026
DA - 2026/09/29
JO - Quantitative imaging in medicine and surgery
DO - 10.21037/qims-2026-0574
UR - https://europepmc.org/article/MED/42997782
AB - 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.
SN - 2223-4292
SN - 2223-4306
ER -