Survival Outcomes and Prognostic Factors in Follicular Lymphoma-Grade 3: A Study Based on the SEER Database
database[Title] 2025-04-23
Sci Prog. 2025 Apr-Jun;108(2):368504251335082. doi: 10.1177/00368504251335082. Epub 2025 Apr 17.
ABSTRACT
ObjectivesFollicular lymphoma-grade 3 is an aggressive subtype of Follicular lymphoma with a higher recurrence risk and poorer survival outcomes compared to lower-grade Follicular lymphoma. Existing prognostic models often lack accuracy due to disease heterogeneity and insufficient integration of demographic and treatment variables. This study aimed to identify independent prognostic factors and develop a nomogram for predicting OS in Follicular lymphoma-grade 3 patients using the SEER database.MethodsThis study is a retrospective cohort study. Data from 1026 Follicular lymphoma-grade 3 patients diagnosed between 2016 and 2021 were extracted from the SEER database. Patients were randomly divided into training (n = 718) and validation (n = 308) cohorts. Prognostic factors were identified using RSF, LASSO regression, and the Boruta algorithm. A multivariate Cox regression model was used to identify independent prognostic factors, which were incorporated into a nomogram. The model's performance was evaluated using ROC curves, calibration curves, and DCA.ResultsAge, radiotherapy, and liver metastasis were identified as independent prognostic factors for OS. The nomogram demonstrated strong predictive performance with AUC values exceeding 0.7 at 12, 36, and 60 months in both cohorts. Calibration curves confirmed the agreement between predicted and observed OS rates. Risk stratification using the nomogram identified significant survival differences between low-risk and high-risk groups (P < 0.05).ConclusionThis study developed a validated nomogram for Follicular lymphoma-grade 3 that integrates clinical, demographic, and treatment factors, offering superior predictive accuracy over traditional staging systems. The model provides a reliable tool for individualized prognosis assessment and treatment optimization in clinical practice.
PMID:40247602 | DOI:10.1177/00368504251335082