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.

OBJECTIVE: To investigate the incidence, clinical and genetic characteristics of pediatric lymphoma patients of China with inborn errors of immunity (IEI)-related gene mutations, which have not been fully studied.
METHODS: From Jan. 2020 to Mar. 2023, IEI-related genetic mutations were retrospectively explored in 108 children with lymphomas admitted to Beijing Children\'s Hospital by NGS. Genetic rule and clinical characteristics as well as treatment outcomes were compared between patients with or without IEI-related gene mutations.
RESULTS: A total of 17 patients (15.7 %) harbored IEI-associated mutations, including 4 cases with X-linked lymphoproliferative syndrome (XLP), 3 cases had mutations in tumor necrosis factor receptor superfamily 13B (TNFRSF13B), 2 cases with Activated p110 syndrome (APDS). Patients with IEI all had alteration of immunocompetence with decreased levels of immunoglobulin and lymphocyte subsets. Recurrent infection existed in 41.2 % of patients. The 18-month event-free survival (EFS) and the overall response rate (ORR) of patients with IEI are significantly lower than those without IEI (33.86% vs. 73.26 %, p = 0.011; 52.94% vs. 87.91 %, p = 0.002, respectively). In addition, patients with IEI had a higher progression disease (PD) rate of 23.5 % than those without IEI of 4.4 % (p = 0.006).
CONCLUSIONS: The present study demonstrated that IEI-associated lymphomas were much more common than originally appreciated in pediatric lymphomas, and those were insensitive to treatment and more likely to progress or relapse. The genomic analysis and a thorough review of the medical history of IEI can be used to distinguish them from pediatric lymphomas without IEI, which are beneficial for the early diagnosis and direct intervention.

OBJECTIVE: Survivors after pediatric Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) are with lifetime risk for second primary malignancy (SPM). This necessitates a thorough analysis to better understand the potential long-term health implications for these individuals.
METHODS: We used a US-wide population-based cancer registry data to quantify the SPM risk and identify its incidence patterns among pediatric lymphoma patients.
RESULTS: We observed 4.74-fold (95% CI 4.27-5.25) and 3.40-fold (95% CI 2.78-4.10) increased risks of SPM in survivors after pediatric HL and NHL, respectively. Through over 40 years\' follow-up, the cumulative incidence of SPM for pediatric lymphoma was persistently increasing, and here we firstly report the high 40-year cumulative incidence rates of SPM, 22.2% for HL and 12.6% for NHL, suggesting that SPM accounts for a great proportion of deaths among survivors. Of 6805 pediatric lymphomas, 462 (6.36%) developed a SPM, especially second breast and thyroid cancer, followed by hematologic neoplasms including leukemia and NHL. The competing risk analysis demonstrated gender, lymphoma subtype and radiotherapy were significantly associated with SPM. Different risk patterns of SPM were identified between pediatric HL and NHL. Chemotherapy accelerated SPM development but did not increase its incidence risk.
CONCLUSIONS: Overall, patients after pediatric lymphoma can be with high lifetime risk of SPM, and more attention should be paid to SPM-related signs for early detection and intervention.

Non-anaplasticperipheral T-cell lymphomas (PTCL) are rare tumors in children, adolescents, and young adults (CAYA) with poor prognosis and scarce genetic data. We analyzed lymphoma tissue from 36 patients up to 18 years old with PTCL, not otherwise specified (PTCL-NOS), hepatosplenic T-cell lymphoma, Epstein-Barr virus (EBV)-positive T-lymphoproliferative diseases, subcutaneous panniculitis-like T-cell lymphoma, and other PTCL types. Twenty-three patients (64%) had at least one genetic variant detectable, including TET2, KMT2C, PIK3D, and DMNT3A. TP53 and RHOA variants, commonly found in adults, were not identified. Eight of 20 (40%) CAYA PTCL-NOS had no detectable mutations. The genetic findings suggest that CAYA PTCL differ from adult cases.

The aim of this study was to analyze whether the baseline metabolic parameters of 18F-FDG PET/CT in pediatric patients with Burkitt lymphoma (BL) can predict treatment response and prognosis. We retrospectively analyzed 68 pediatric patients with BL who underwent PET/CT before treatment. PET images were analyzed semi-quantitatively by measuring the maximum standardized uptake (SUVmax), total metabolic tumor volume (tMTV), and total lesion glycolysis (TLG). Survival curves were plotted according to the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards regression models were used to assess the relation between potential variables and outcomes. tMTV and TLG were significantly lower in patients with complete response compared with those with partial response at the end of treatment. PET metabolic parameters (tMTV and TLG) were the independent prognostic values for outcome. TMTV and TLG were significantly connected with treatment response and prognosis in pediatric with BL.