Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas
Rattachement africain : kr, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalance and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n=1,068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox) and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models—XGBoost-Cox, Lasso-Cox, and GBM-Cox—were assessed on an independent test set (n=92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden’s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: 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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas
- Date Crossref
- 12/08/2025
- Éditeur
- MDPI AG
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
The Catholic University of Korea Yeouido St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
Catholic University of Korea pays non établi dans la noticeUniversité ou école supérieure
-
The Catholic University of Korea Seoul St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
The Catholic University of Korea Bucheon St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
The Catholic University of Korea Incheon St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
The Catholic University of Korea Uijeongbu St. Mary's Hospital pays non établi dans la noticeÉtablissement de santé
-
College of Medicine Department of Hematology pays non établi dans la noticeUniversité ou école supérieure
The Catholic University of Korea Yeouido St. Mary's Hospital, Catholic University of Korea et The Catholic University of Korea Seoul St. Mary's Hospital, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.