Background/Objectives: Ovarian cancer (OC) is frequently diagnosed at an advanced stage due to the nonspecific nature of its symptoms. While population-wide screening has failed to reduce mortality, timely diagnosis in symptomatic women remains a promising and underutilized strategy to improve clinical outcomes. The aim of this study was to develop a sensitive, scalable biomarker assay to improve early-stage detection in symptomatic women. Methods: A multi-omic diagnostic model was developed using serum samples from symptomatic women. Lipidomic profiles were generated by liquid chromatography–mass spectrometry (LC-MS), and protein levels were measured using immunoassays. Statistical and machine learning approaches were applied to assess diagnostic performance across disease stages and subtypes. Results: The multi-omic model demonstrated robust performance across a clinically challenging population, with both lipid and protein data necessary for detecting OC across a range of stages and subtypes. The model achieved 98.7% sensitivity in early-stage OC and 98.6% across a range of OC subtypes and stages at 70% fixed specificity, which represented significant improvements over CA125 in the same cohort. In addition, in a small subset of samples, lipid and protein profiles from OC patients undergoing treatment differed from untreated patients and controls, suggesting that this approach may also be useful in other aspects of clinical management, such as treatment monitoring. Conclusions: This multi-omic assay offers a promising solution to accelerate diagnosis, improve early detection, and potentially reduce OC mortality.
Law et al. (Tue,) studied this question.
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