Early and accurate detection of ovarian cancer by profiling cell-free mitochondrial DNA fragmentomics in multi-ethnic population

Ovarian cancer (OC) remains highly lethal due to frequent late-stage diagnosis and the challenge of distinguishing malignant from benign adnexal masses (BAM) preoperatively. Reliable non-invasive diagnostic biomarkers remain an unmet clinical need. This multi-center study aimed to evaluate the diagnostic potential of plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics for improving OC management. We developed two machine learning models based on ccf-mtDNA features: an OC Detection (OD) model to distinguishing OC from healthy controls (HC), and a Benign vs. Malignant Differential diagnosis (BMD) model to discriminate OC from BAM. The OD model achieved an area under the curve (AUC) of 0.987 in the training cohort and maintained high performance (AUC above 0.979) in internal and external validation cohorts, with 91.67% sensitivity for Stage I OC at 95% specificity. The BMD model demonstrated strong discriminatory power (AUC = 0.982) and generalizability (AUC ranging from 0.961 to 0.980 in four validation cohorts), significantly outperforming serum biomarkers. Remarkably, both models performed robustly in the Chinese minority cohort. These findings establish ccf-mtDNA fragmentomics as a powerful liquid biopsy approach for the early and accurate detection of OC, with significant potential for clinical translation in high-risk populations and differential diagnosis of benign vs. malignant adnexal masses.