Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation

Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation

IntroductionHigh-grade serous ovarian cancer (HGSOC) is the most aggressive and prevalent subtype of ovarian Treatment outcomes are significantly influenced by residual disease status following neoadjuvant chemotherapy (NACT). Predicting residual disease before surgery can improve patient stratifica...

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Bibliographic Details
Main Authors: Seyma Yasar, Rauf Melekoglu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Medicine
Subjects:
high-grade serous ovarian cancer (HGSOC)
neoadjuvant chemotherapy (NACT)
machine learning
proteomic biomarkers
SHAP analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/full
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