Supplemental Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Brendan M. Giles; Rachel Culp‐Hill; Robert Adger Law; Charles M. Nichols; Melvyn Goldberg; Enkhtuya Radnaa; Maria Pik Wong; Connor Hansen; Moises Zapata; Collin Hill; Kian Behbakht; Benjamin G. Bitler; Emma J. Crosbie; Chloe E. Barr; Anna Jeter; Vuna S. Fa; Violeta Beleva Guthrie; Leonardo N. Hagmann; Emily C. Kubota; James R. White; Abigail McElhinny

doi:10.1158/2767-9764.30051281

Key Points

Discovery-based lipidomics analysis improves early detection of ovarian cancer in symptomatic patients, enhancing diagnostic accuracy.
The incorporation of protein biomarkers with lipidomics could facilitate earlier identification of ovarian cancer, potentially leading to better outcomes.
Machine learning algorithms are utilized to analyze data, which may boost predictive capabilities in identifying at-risk patients.
These findings highlight the promising role of combining lipidomic and protein data for effective cancer screening strategies.

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Implication

Research reveals the potential of lipidomics and protein biomarkers for early detection of ovarian cancer, suggesting valuable tools for the symptomatic population.

Abstract

Supplemental Figure 1. Discovery-based lipidomics feature filtering strategy

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Giles et al. (2025) studied this question.

synapsesocial.com/papers/68c18c089b7b07f3a06147b4 https://doi.org/10.1158/2767-9764.30051281

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Supplemental Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

Supplemental Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

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