PDF(Pubmed)
Abstract：
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,871 pediatric and 226,211 adult patients, identified key risk factors for pediatric lymphoma survival, developed a predictive nomogram, and utilized machine learning to predict long-term lymphoma-specific mortality risk. Between 1975 and 2018, we observed substantial increases in 1-year (19.3%), 5-year (41.9%), and 10-year (48.8%) overall survival rates in pediatric patients with lymphoma. Prognostic factors such as age, sex, race, Ann Arbor stage, lymphoma subtypes, and radiotherapy were incorporated into the nomogram. The nomogram exhibited excellent predictive performance with area under the curve (AUC) values of 0.766, 0.724, and 0.703 for one-year, five-year, and ten-year survival, respectively, in the training cohort, and AUC values of 0.776, 0.712, and 0.696 in the validation cohort. Importantly, the nomogram outperformed the Ann Arbor staging system in survival prediction. Machine learning models achieved AUC values of approximately 0.75, surpassing the conventional method (AUC = ~ 0.70) in predicting the risk of lymphoma-specific death. We also observed that pediatric lymphoma survivors had a substantially reduced risk of lymphoma after ten years b,ut faced an increasing risk of non-lymphoma diseases. The study highlights substantial improvements in pediatric lymphoma survival, offers reliable predictive tools, and underscores the importance of long-term monitoring for non-lymphoma health issues in pediatric patients.
摘要：
小儿霍奇金和非霍奇金淋巴瘤在生物学和治疗上与成人病例不同，然而，缺乏针对小儿淋巴瘤的生存分析。我们分析了1975年至2018年的淋巴瘤数据，比较了7,871名儿童患者和226,211名成人患者的生存趋势。确定了儿童淋巴瘤生存的关键危险因素，开发了一个预测列线图，并利用机器学习来预测长期淋巴瘤特异性死亡风险。在1975年至2018年期间，我们观察到1年内大幅增长(19.3%)，5年期（41.9%），儿科淋巴瘤患者的10年总生存率（48.8％）。预后因素，如年龄，性别,种族,安阿伯舞台,淋巴瘤亚型,和放疗被纳入列线图。列线图表现出出色的预测性能，一年的曲线下面积（AUC）值为0.766、0.724和0.703，五年,十年的生存，分别,在训练组中，验证队列中的AUC值为0.776、0.712和0.696。重要的是,列线图在生存预测方面优于AnnArbor分期系统。机器学习模型在预测淋巴瘤特异性死亡风险方面实现了约0.75的AUC值，超过了常规方法（AUC=〜0.70）。我们还观察到儿科淋巴瘤幸存者在10年后患淋巴瘤的风险大大降低。UT面临非淋巴瘤疾病的风险越来越大。该研究强调了小儿淋巴瘤生存率的实质性改善，提供可靠的预测工具，并强调了长期监测儿科患者非淋巴瘤健康问题的重要性.
