Ovarian cancer remains a leading cause of cancer-related deaths in women. Early detection improves prognosis, but current diagnostic tools still need improvement. We aimed to identify high-risk patient profiles for ovarian cancer using cluster analysis of age and tumor marker data.

A secondary dataset analysis was conducted using unsupervised learning techniques. Data were from a University Hospital, originally collected between July 2011 and July 2018 in Taiwan. In total, 349 women diagnosed with ovarian masses, including both benign and malignant tumors, were included in this analysis. The median age was 45 years, and 49 % were diagnosed with ovarian cancer in pathology. We used a hierarchical clustering algorithm to find groups of patients with similar features.

Two clusters were identified (N = 204 and 145), with a high-risk cluster (66.2 % malignancy) characterized by significantly older age, higher CA125, HE4, CEA, and AFP levels, and a lower CA19–9 level than the low-risk cluster (24.8 % malignancy). The assessment of clustering stability and internal validity yielded a figure of merit score of 0.970 and a silhouette coefficient of 0.524. A classification model using age, CA125, HE4, and CA19–9 demonstrated high accuracy (89.4 %), sensitivity (94.5 %), specificity (83.7 %), and a large area under the curve (89.1 %) for the risk stratification.

Integrating tumor markers with patient demographics improved the differentiation between benign and malignant ovarian masses. This approach can help clinicians prioritize high-risk patients for further diagnostic evaluation and reduce unnecessary invasive procedures for low-risk patients.