ArticleCell reports. Medicine2024
Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A cfDNA fragmentomics classifier for noninvasive differentiation of benign and malignant renal masses.Molecular biomedicine · 2026Article
- Circulating Tumor DNA Sequencing for Early Diagnosis and Detection of Treatment Response, Minimal Residual Disease, and Recurrence in Gastroesophageal Cancer.Journal of gastrointestinal cancer · 2026Review
- ALFAssay: A feed‑forward neural network for quantitative fragmentomics‑based ctDNA profiling in breast cancer.PLoS computational biology · 2026Article
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Comprehensive review of publicly accessible databases for gastrointestinal cancer and future challenges in database construction.Discover oncology · 2026Review
- Liquid Biopsy and Nonendoscopic Biomarkers for Precision Early Detection of Esophageal Cancer: Evidence, Risk Stratification and Translational Challenges.Journal of Cancer · 2026Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- From Combination Early Detection to Multicancer Testing: Shifting Cancer Care toward Proactive Prevention and Interception.Cancer prevention research (Philadelphia, Pa.) · 2025Review
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.Frontiers in immunology · 2025Article
- Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.Drug design, development and therapy · 2025Article
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Authors and funding
19 authors.
Funding
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Abstract
In this study, we develop a stacked ensemble model that utilizes cell-free DNA (cfDNA) fragmentomics for the early detection of esophageal squamous cell carcinoma (ESCC). This model incorporates four distinct fragmentomics features derived from whole-genome sequencing (WGS) and advanced machine learning algorithms for robust analysis. It is validated across both an independent validation cohort and an external cohort to ensure its generalizability and effectiveness. Notably, the model maintains its robustness in low-coverage sequencing environments, demonstrating its potentials in clinical settings with limited sequencing resources. With its remarkable sensitivity and specificity, this approach promises to significantly improve the early diagnosis and management of ESCC. This study represents a substantial step forward in the application of cfDNA fragmentomics in cancer diagnostics, emphasizing the need for further research to fully establish its clinical efficacy.
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