Supplemental Table 3 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Key Points

Machine learning improves early detection of ovarian cancer among symptomatic individuals, enhancing diagnostic methods.
The approach integrates lipidomics and protein biomarkers to optimize screening techniques for ovarian cancer.
A new cohort subset was established to refine the analysis and support the study’s findings on cancer detection efficacy.
Findings indicate a promising direction for future screening processes, suggesting improvements in standard diagnostic protocols.

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Machine learning reveals potential for early ovarian cancer detection using lipidomics and protein biomarkers in symptomatic population.

Supplemental Table 3. Normalization Subset of Cohort 1

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Giles et al. (2025) studied this question.

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