Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas
This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL.
Authors
- Jong Hyuk Lee (ORCID: https://orcid.org/0000-0001-8465-2791)
- Young‐Woo Jeon (ORCID: https://orcid.org/0000-0003-3362-8200)
- Seoree Kim (ORCID: https://orcid.org/0000-0001-7461-103X)
- Tae Jung Kim (ORCID: https://orcid.org/0000-0003-3140-3681)
- Seok‐Goo Cho (ORCID: https://orcid.org/0000-0002-5429-4839)
- Eun Ji Han (ORCID: https://orcid.org/0000-0002-9041-9977)
- Gi June Min (ORCID: https://orcid.org/0000-0001-7636-4693)
- Hye Sung Won (ORCID: https://orcid.org/0000-0002-5134-4538)
- Byung‐Su Kim (ORCID: https://orcid.org/0000-0003-4014-9400)
- Tong Yoon Kim (ORCID: https://orcid.org/0000-0002-9559-992X)
- Joon Won Jeoung
Institutions
- St. Mary's Hospital (US)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- St. Mary's Hospital (US)
- St Mary's Hospital (GB)
- The Catholic University of Korea Bucheon St. Mary's Hospital (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Cancers
- Published
- 2025-09-09
- DOI
- https://doi.org/10.3390/cancers17182952
- Primary Topic
- Lymphoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00