Early and accurate detection of ovarian cancer by profiling cell-free mitochondrial DNA fragmentomics in multi-ethnic population.
A blood test measuring fragments of mitochondrial DNA can detect ovarian cancer early with high accuracy across diverse populations, potentially enabling earlier intervention.
This multi-center study developed and validated two machine-learning models based on plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics for early ovarian cancer detection, achieving >0.979 AUC across all validation cohorts. The approach offers a non-invasive liquid biopsy method that outperforms conventional serum biomarkers and works robustly in ethnically diverse populations.
What the study was
- Study design
- Multi-center diagnostic biomarker validation study with machine-learning models; multiple external validation cohorts
- Population
- Women undergoing evaluation for adnexal masses or ovarian cancer, multi-ethnic cohorts (Han Chinese and minority populations)
- Sample size
- Multiple cohorts (training + 4 external validation cohorts including Chinese minority cohort)
- Category
- Early Detection
- Maturity
- Validated
- Journal
- Genome Medicine
Why it surfaced
Novel ccf-mtDNA fragmentomics approach for Stage I ovarian cancer detection with high AUC validated across multiple external cohorts including multi-ethnic populations; addresses critical unmet need for early OC detection.
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