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...
Tallennettuna:
| Päätekijät: | , |
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| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Frontiers Media S.A.
2025-07-01
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| Sarja: | Frontiers in Medicine |
| Aiheet: | |
| Linkit: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/full |
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