ArticleCancers2023
Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.BioMed research international · 2025Pooled it
- Liquid biopsy in non-small cell lung cancer: a meta-analysis of state-of-the-art and future perspectives.Frontiers in genetics · 2023Pooled it
- Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.Scientific reports · 2026Article
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics.Diagnostics (Basel, Switzerland) · 2025Review
- Biomarkers for non-small cell lung cancer risk using multi-omics approaches: a nested case-control study.Translational lung cancer research · 2025Article
- In Search of Ideal Solutions for Cancer Diagnosis: From Conventional Methods to Protein Biomarkers in Liquid Biopsy.Proteomes · 2025Review
- Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study.JMIR bioinformatics and biotechnology · 2025Article
- Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.IEEE access : practical innovations, open solutions · 2025Article
- Mechanism research on inhibition of gastric cancerOpen medicine (Warsaw, Poland) · 2025Article
- Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology.Frontiers in molecular biosciences · 2025Review
- Vaccine-based therapeutic interventions in lung cancer management: A recent perspective.Medical oncology (Northwood, London, England) · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.
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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.