ArticleAnalytical and bioanalytical chemistry2024
Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.
Article in Analytical and bioanalytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Raman spectroscopy in the detection and diagnosis of lung cancer: a meta-analysis.Lasers in medical science · 2025Pooled it
- Emerging Plasmonic Nanomaterials for SERS-Based Disease Diagnostics: Innovations, Clinical Challenges, and AI Integration.Molecules (Basel, Switzerland) · 2026Review
- The Dual Roles of Extracellular Vesicle Subtypes in Regulating Traumatic Brain Injury.International journal of molecular sciences · 2026Review
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 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
- Diagnostic Potential of Exosomes in Colorectal Cancer: Current Advances and Future Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Plasmonic Nanostructures for Exosome Biosensing: Enabling High-Sensitivity Diagnostics.Nanomaterials (Basel, Switzerland) · 2025Review
- Harnessing Surface-Enhanced Raman Spectroscopy for Breath-Based Diagnostics.Analytical chemistry · 2025Article
- Exosomes in Precision Oncology and Beyond: From Bench to Bedside in Diagnostics and Therapeutics.Cancers · 2025Review
- Recent Exploration of Solid Cancer Biomarkers Hidden Within Urine or Blood Exosomes That Provide Fundamental Information for Future Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2025Review
- Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface-Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms.Advanced sensor research · 2025Article
- Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025Review
- Overcoming the Delivery Challenges in CRISPR/Cas9 Gene Editing for Effective Cancer Treatment: A Review of Delivery Systems.International journal of medical sciences · 2025Review
- Hot-spot empowered gold nanoparticles for theranostics in breast cancer.Theranostics · 2025Review
- Role of Exosomes in Salivary Gland Tumors and Technological Advances in Their Assessment.Cancers · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.
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39017700What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.