ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules.The Journal of international medical research · 2023Trial
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Circulating RNA as a Functional Component of Liquid Biopsy in Cancer: Concepts, Classification, and Clinical Applications.International journal of molecular sciences · 2026Review
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Identification of High-Performing Blood Metabolite Biomarkers of Lung Cancer in a Chinese Population.Phenomics (Cham, Switzerland) · 2026Article
- From Optical to Molecular Imaging on Human Skin: A Review.Chemical & biomedical imaging · 2026Review
- Harnessing multi-omics approaches to decipher tumor evolution and improve diagnosis and therapy in lung cancer.Biomarker research · 2025Review
- Biomarkers for the diagnosis of indeterminate pulmonary nodules: are we there yet?Journal of thoracic disease · 2025Review
- Vessel-On-A-Chip Coupled Proteomics Reveal Pressure-Overload-Induced Vascular Remodeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity.Journal of imaging informatics in medicine · 2025Article
- 3D‑printed template‑guided iodine‑125 seed implantation to treat complete occlusion of the superior vena cava in pulmonary sarcomatoid carcinoma: A case report.Oncology letters · 2024Article
- Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.Respiratory research · 2024Article
- AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.iScience · 2024Article
- Article
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- Association between CD4Oncology letters · 2024Article
- MIS18A upregulation promotes cell viability, migration and tumor immune evasion in lung adenocarcinoma.Oncology letters · 2024Article
- Role of angiomotin family members in human diseases (Review).Experimental and therapeutic medicine · 2024Review
- Intrapleural perfusion hyperthermia improves the efficiency of anti‑PD1 antibody‑based therapy for lung adenocarcinoma: A case report.Oncology letters · 2024Article
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20 authors.
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Abstract
Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.
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