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Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

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机构: [1]Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. [2]Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [3]State Key Laboratory of Biotherapy, Sichuan University, Chengdu, Sichuan 610041, China. [4]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China. [5]Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China. [6]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [7]Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China. [8]Department of Thoracic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China. [9]Tailai Inc, Chengdu, Sichuan, China. [10]Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China. [11]Department of Thoracic Surgery, Hwa Mei Hospital, Chinese Academy of Sciences, Zhejiang, China. [12]Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China. [13]Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [14]Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
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Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions.© 2024. The Author(s).

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大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
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第一作者机构: [1]Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
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通讯机构: [2]Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [3]State Key Laboratory of Biotherapy, Sichuan University, Chengdu, Sichuan 610041, China.
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