ArticleScientific reports2024
Precision cancer classification using liquid biopsy and advanced machine learning techniques.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends and hotspots of circulating tumor DNA in colorectal cancer: a bibliometric study.Frontiers in oncology · 2025Pooled it
- The role of serologic biomarkers "liquid biopsy" in the fight against breast cancer: From biological foundations to clinical impact.iScience · 2026Review
- Review
- ALFAssay: A feed‑forward neural network for quantitative fragmentomics‑based ctDNA profiling in breast cancer.PLoS computational biology · 2026Article
- Chemical and Technological Aspects of Magnetic Particles for Cancer Liquid Biopsy Applications.ChemistryOpen · 2026Review
- Hybrid I-ResNeT-ViT and Cost-Sensitive InceptionV3 Models for Tumour Severity and Malignancy Classification Using Medical Imaging.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Circulating RNA as a Functional Component of Liquid Biopsy in Cancer: Concepts, Classification, and Clinical Applications.International journal of molecular sciences · 2026Review
- Harnessing viral footprints in circulating free DNA (cfDNA) for early cancer detection: A focus on liquid-biopsy-based screening.International journal of cancer · 2026Review
- Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach.Discover oncology · 2026Article
- Integrating clinical features withBMC medical imaging · 2025Article
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Evolutionary Overview and Future Perspectives: ESR1 Mutations, Liquid Biopsy, and Artificial Intelligence for a New Era of Personalized Medicine in ER+ Breast Cancer.Molecular diagnosis & therapy · 2025Review
- ctDNA in Pancreatic Adenocarcinoma: A Critical Appraisal.Current oncology (Toronto, Ont.) · 2025Review
- Review
- Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Liquid Biopsy: The Challenges of a Revolutionary Approach in Oncology.International journal of molecular sciences · 2025Review
- Impact of clinical factors on accuracy of ovarian cancer detection via platelet RNA profiling.Blood advances · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering a crucial window for timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations in circulating cell-free, circulating tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as a tool for early detection. This study focuses on cancer detection using mutations in plasma cfDNA/ctDNA and protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature's relevance to the target variable, eliminating redundant features to improve efficiency. The eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects the top ten features, resulting in a 60% dataset dimensionality reduction. The Light Gradient Boosting Machine (LGBM) model is employed for classification, optimizing its performance through a random search for hyper-parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross-validation, weighted by their respective balanced accuracy, and averaged to get final predictions. Applying this methodology, the proposed system achieves 99.45% accuracy and 99.95% AUC for detecting the presence of cancer while achieving 93.94% accuracy and 97.81% AUC for cancer-type classification. Our methodology leads to enhanced healthcare outcomes for cancer patients.
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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.