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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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Principais autores: Seyma Yasar, Rauf Melekoglu
Formato: Artigo
Idioma:Inglês
Publicado em: Frontiers Media S.A. 2025-07-01
coleção:Frontiers in Medicine
Assuntos:
high-grade serous ovarian cancer (HGSOC)
neoadjuvant chemotherapy (NACT)
machine learning
proteomic biomarkers
SHAP analysis
Acesso em linha:https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/full
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