A novel decision tree model based on chromosome imbalances in cell-free DNA and CA-125 in the differential diagnosis of ovarian cancer.

Objective: CA-125 is widely used as biomarker of ovarian cancer. However, CA-125 suffers low accuracy. We developed a hybrid analytical model, the Ovarian Cancer Decision Tree (OCDT), employing a two-layer decision tree, which considers genetic alteration information from cell-free DNA along with CA-125 value to distinguish malignant tumors from benign tumors.

Methods: We consider major copy number alterations at whole chromosome and chromosome-arm level as the main feature of our detection model. Fifty-eight patients diagnosed with malignant tumors, 66 with borderline tumors, and 10 with benign tumors were enrolled.

Results: Genetic analysis revealed significant arm-level imbalances in most malignant tumors, especially in high-grade serous cancers in which 12 chromosome arms with significant aneuploidy (P<0.01) were identified, including 7 arms with significant gains and 5 with significant losses. The area under receiver operating characteristic curve (AUC) was 0.8985 for copy number variations analysis, compared to 0.8751 of CA125. The OCDT was generated with a cancerous score (CScore) threshold of 5.18 for the first level, and a CA-125 value of 103.1 for the second level. Our most optimized OCDT model achieved an AUC of 0.975.

Conclusions: The results suggested that genetic variations extracted from cfDNA can be combined with CA-125, and together improved the differential diagnosis of malignant from benign ovarian tumors. The model would aid in the pre-operative assessment of women with adnexal masses. Future clinical trials need to be conducted to further evaluate the value of CScore in clinical settings and search for the optimal threshold for malignancy detection.