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May 29, 2026Journal of Clinical Oncology

A first-of-its-kind multi-omic assay using lipids and proteins to identify early-stage ovarian cancer in symptomatic individuals.

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Authors

AMAbigail McElhinnyRCRachel Culp-HillBGBrendan Giles

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Overview

Randomized trial demonstrates enhanced early-stage ovarian cancer detection in symptomatic women, suggesting improved diagnostics.

Key Points

  • Evaluate the effectiveness of a multi-omic assay combining lipids and proteins for early-stage ovarian cancer diagnosis in symptomatic individuals.
  • Conducted a large-scale, prospective clinical trial with N=346 symptomatic women
  • Utilized mass spectrometry to quantify serum lipids and clinically validated immunoassays for proteins
  • Applied ensemble machine learning modeling to refine biomarkers for ovarian cancer detection.
  • Achieved 96.5% sensitivity and 80.6% specificity for ovarian cancer detection versus symptomatic controls
  • Demonstrated 92% sensitivity specifically for early-stage ovarian cancer
  • Substantially improved OC detection rates compared to current standard biomarkers.

Cite This Study

McElhinny et al. (2026) studied this question.

synapsesocial.com/papers/6a192f07fab5b468c441853dhttps://doi.org/10.1200/jco.2026.44.16_suppl.5552
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