Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

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.