Identifying key prognostic indicators in Wilms tumor using m…
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- DOI 10.1200/jco.2025.43.16_suppl.4557
- OpenAlex ID W4410822621
- Type Conference-abstract
- Language EN
- Published 28 May 2025
- Volume 43 (16_suppl)
- Pages 4557
- Source Journal of Clinical Oncology
- Publisher Lippincott Williams & Wilkins
- Countries 1
- Institutions 3
- Added 10 Oct 2025
- Updated 01 Oct 2026
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Identifying key prognostic indicators in Wilms tumor using machine learning techniques.
Conference-abstract 2025 EN Bronze
4557 Background: Wilms tumor is a rare pediatric malignancy, accounting for 6% of pediatric tumors and primarily affecting the kidneys. Its impact on quality of life and long-term outcomes complicates management. This study leveraged machine learning (ML) to identify prognostic factors with the aim of enhancing prognosis and survival rates. Methods: Data were obtained from the SEER database (2004-…
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UN Sustainable Development Goals
1 SDGsAuthors & Affiliations
7 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Salsabeel Aljawabrah | University of Jordan (JO) | — |
| 2 | Sakhr Alshwayyat | King Hussein Cancer Center (JO) | |
| 3 | Kholoud Saleh Alqasem | King Hussein Cancer Center (JO) | |
| 4 | Tala Abdulsalam Alshwayyat | Jordan University of Science a… (JO) | |
| 5 | Abdalwahab M.Z.M. Alenezy | Jordan University of Science a… (JO) | — |
| 6 | Mustafa Alshwayyat | Jordan University of Science a… (JO) | — |
| 7 | Noor Almasri | University of Jordan (JO) | — |
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