A nomogram for predicting the cancer-specific death of children and adolescents-onset lymphoma: A SEER database analysis

Medicine (Baltimore). 2025 Aug 8;104(32):e43781. doi: 10.1097/MD.0000000000043781.

ABSTRACT

Lymphoma in children and adolescents represents a distinct clinical entity, often associated with aggressive biological behavior and poor cancer-specific outcomes. Accurate prediction of cancer-specific death (CSD) in this population is essential for guiding personalized treatment and follow-up strategies. This study aimed to develop and validate a nomogram for predicting the risk of CSD in children and adolescents with lymphoma using a competing risk model based on a large population-based cohort. Lymphoma cases diagnosed in patients aged 1 to 17 years were extracted from the surveillance, epidemiology, and end results database. Eligible patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Independent prognostic factors for CSD were identified using univariate and multivariate competing risk regression analyses. A nomogram was constructed based on significant variables, and its performance was evaluated by the concordance index (C-index), receiver operating characteristic curves, and calibration plots. A total of 6954 pediatric and adolescent lymphoma cases were included. Six variables, including age, race, diagnosis time, lymphoma subtype, tumor grade, and tumor stage, were identified as independent predictors of CSD. The nomogram showed strong discriminative power, with 5-, 10-, and 15-year area under curves of 0.814, 0.794, and 0.787 in the training cohort, 0.818, 0.792, and 0.764 in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes. Survival analysis showed that patients with high-risk score had a poor clinical outcome. We developed a robust and clinically practical nomogram for predicting CSD in children and adolescents with lymphoma. This tool may assist clinicians in identifying high-risk patients and formulating individualized management strategies.

PMID:40797401 | PMC:PMC12338206 | DOI:10.1097/MD.0000000000043781