Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances 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= 1068), 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 asTP53,BLM, andRAD50. 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.
背景/目的：滤泡性淋巴瘤（FL）和边缘区淋巴瘤（MZL）是低级别B细胞淋巴瘤（LGBCLs），临床病程惰性，但终生存在向侵袭性淋巴瘤（尤其是弥漫性大B细胞淋巴瘤）发生组织学转化（HT）的风险。由于类别不平衡以及时间依赖性事件固有的复杂性，预测HT具有挑战性。虽然目前存在生存预后指数，但它们并未专门针对HT风险进行评估。本研究旨在开发并验证基于生存分析和传统分类的机器学习模型，以预测队列中的HT事件。方法：利用多中心回顾性数据集（n=1068），比较了生存模型（Cox比例风险模型、Lasso-Cox、随机生存森林、梯度提升Cox模型[GBM-Cox]、极限梯度提升Cox模型[XGBoost-Cox]）和分类模型（逻辑回归、Lasso逻辑回归、随机森林、梯度提升、XGBoost）。在独立测试集（n=92）上评估了表现最佳的生存模型——XGBoost-Cox、Lasso-Cox和GBM-Cox。基于约登指数确定最优二元风险截断点以最大化模型灵敏度。结果：生存模型显示出优于传统分类器的预测性能，其中XGBoost-Cox具有最高的平均准确率（85.3%）、时间依赖性曲线下面积（0.795）、灵敏度（98%）、特异度（83.9%）和一致性指数（0.836）。整合新一代测序（NGS）数据提高了模型的准确率和特异度，表明遗传因素可改善HT预测。主成分分析揭示了与HT风险相关的独特基因突变模式，突出了DNA修复基因如TP53、BLM和RAD50的作用。结论：本研究强调了基于生存分析的机器学习方法结合NGS数据在个性化评估FL和MZL患者HT风险分层方面的临床价值。
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