Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study

Esophageal cancer (EC) is highly lethal due to late diagnosis, and accurate non-invasive tools for early detection and subtyping remain lacking. Circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics offers a promising biomarker source. In this large multi-center study enrolling 1,260 participants spanning healthy control (HC), benign esophageal diseases (BED), precancerous lesions and EC patients, we developed two machine learning models based on plasma ccf-mtDNA fragmentomic profiles: an EC detection (ED) model and a pathological subtyping prediction (PSP) model. The ED model achieved an AUC of 0.9706 (EC vs. non-EC) in the training cohort, with high performance in internal (0.9762), external (0.9748), and Chinese minority (0.9668, Uyghur and Kazakh) validation cohorts. At the optimal cut-off, it yielded 92.16% sensitivity for stage I EC in the combined validation cohort and correctly identified 93.10% of early EC cases missed by preoperative biopsy. It also discriminated high-grade intraepithelial neoplasia from non-EC with an AUC of 0.9597. The PSP model distinguished ESCC from EAC with 89.71% accuracy, improving to 90.74% after integrating key clinical parameters. In summary, ccf-mtDNA fragmentomics provides a robust, non-invasive platform for early EC detection, precancerous risk stratification, and histological subtyping, demonstrating strong potential to transform clinical management.