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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and

Qiong Li1, Hongde Liu1, Jinke Wang1

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.

Technology in Cancer Research & Treatment
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning analysis of cell-free DNA shows high specificity and moderate-to-high sensitivity for multi-cancer early detection in independent validation studies. Performance varies by study design and population, requiring further large-scale validation before clinical use.

Keywords:
cell-free DNAearly diagnosisliquid biopsymachine learningmeta-analysismethylation biomarkersmulti-cancer detectionnon-invasive screening

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Area of Science:

  • Biomarkers
  • Machine Learning
  • Oncology

Background:

  • Machine learning (ML)-based analysis of cell-free DNA (cfDNA) is a promising strategy for multi-cancer early detection (MCED).
  • Existing performance estimates often stem from training or enriched cohorts, limiting real-world applicability.
  • Independent validation is crucial to assess the true diagnostic accuracy of these assays.

Purpose of the Study:

  • To conduct a systematic review and meta-analysis of ML-based cfDNA assays for MCED.
  • To evaluate diagnostic accuracy using only independent validation or testing datasets.
  • To identify factors influencing assay performance and heterogeneity.

Main Methods:

  • Systematic review and diagnostic accuracy meta-analysis of 13 studies.
  • Inclusion of 23 independent datasets from 14,892 participants, excluding all training data.
  • Bivariate random-effects model used to estimate pooled sensitivity, specificity, and diagnostic odds ratio (DOR), with subgroup analyses to explore heterogeneity.

Main Results:

  • Pooled sensitivity of 0.78 (95% CI: 0.66-0.87) and pooled specificity of 0.96 (95% CI: 0.90-0.98).
  • Summary area under the curve (AUC) of 0.94 and DOR of 76.6.
  • Significant heterogeneity (I² > 90%) observed, influenced by geographic region, sample size, and cfDNA biomarker type.

Conclusions:

  • ML-based cfDNA assays exhibit high specificity and moderate-to-high sensitivity in independent validation settings for MCED.
  • Diagnostic performance is context-dependent, influenced by study design, population characteristics, and analytical choices.
  • Large-scale, prospective, population-based validation is necessary prior to widespread clinical implementation.