ArticleNature communications2025
Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- [Application and Progress of Organoid-on-a-chip Platforms in Lung Cancer Diagnosis and Therapy].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Pooled it
- Circulating Cell-free DNA as a biomarker for radiation-induced injury: From mechanistic insights to clinical translation.Cancer metastasis reviews · 2026Review
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Should we optimise or revise the management of initially unresectable stage III non-small cell lung cancer: rethinking consolidation therapy.Journal of the National Cancer Center · 2026Article
- Epigenetics in lung cancer precision medicine: from bench to bedside-a narrative review.Translational lung cancer research · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.Biomedical engineering online · 2026Article
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.NPJ digital medicine · 2026Article
- Artificial intelligence and multiomics integration for Parkinson's disease drug development.Molecules and cells · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.World journal of surgical oncology · 2026Review
- Improving risk stratification of pulmonary nodules: an integrated perinodular vascular and radiomic model for clinical decision support.BMC medical imaging · 2026Article
- The value of an integrated multi-omics model in the diagnosis of benign and malignant pulmonary nodules.Translational cancer research · 2026Article
- Clinical and biological features of CMV reactivation in ARDS: a prospective cohort study.Critical care (London, England) · 2026Article
- B3GNT6-Linked Multimodal Signatures Integrate Tissue Morphology and PTM-Related Transcriptomics to Stratify Tumor.International journal of biological sciences · 2026Article
- Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.Journal of thoracic disease · 2025Review
- Harnessing multi-omics approaches to decipher tumor evolution and improve diagnosis and therapy in lung cancer.Biomarker research · 2025Review
- Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study (DECIPHER-NODL).The Lancet regional health. Western Pacific · 2025Article
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Authors and funding
26 authors.
Funding
Abstract
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.
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