Preoperative discrimination of malignant and benign ovarian masses using cfDNA fragmentomics and serum biomarkers

Abstract Preoperative discrimination of ovarian cancer from benign masses poses significant challenges, particularly in cases where serum biomarkers yield negative or nonspecific results. This multicenter observational diagnostic modeling study analyzed plasma cell-free DNA (cfDNA) from 743 participants across training, internal test, external test, and two exploratory cohorts using low-pass whole-genome sequencing. Four fragmentomic feature classes—fragment size patterns, nucleosome footprint-related features, copy-number variations, and repeat element patterns—were incorporated into a stacked machine-learning model. Model development and cutoff selection were limited to the training cohort, followed by evaluation in the internal-test and external-test cohorts. The cfDNA model achieved areas under the receiver operating characteristic curve (AUCs) of 0.930 (95% confidence interval [CI], 0.894–0.966) and 0.932 (95% CI, 0.891–0.972) in the internal-test and external-test cohorts, respectively. Incorporation of cfDNA with cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) increased AUCs to 0.964 (95% CI, 0.938–0.990) and 0.964 (95% CI, 0.938–0.991). At cutoffs derived from the training cohort, the combined model maintained sensitivities of 88.4% and 89.2% while enhancing specificity from 77.9% to 88.4% and from 78.9% to 91.5%, respectively. The cfDNA model also effectively detected cancers with CA125 and/or HE4 levels below conventional thresholds, and post hoc analyses demonstrated complementary discrimination when cfDNA was added to the Risk of Ovarian Malignancy Algorithm. These findings advocate for prospective evaluation of integrated cfDNA fragmentomics as an adjunct for preoperative risk stratification of ovarian masses.