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Development and validation of a survival nomogram in patients with primary testicular diffuse large B-cell lymphoma
OBJECTIVE: We developed and validated a nomogram for overall survival (OS) and cancer-specific survival (CSS) prediction in patients with primary testicular diffuse large B-cell lymphoma (PT-DLBCL). METHODS: Patients diagnosed with PT-DLBCL were selected from the Surveillance, Epidemiology, and End...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492492/ https://www.ncbi.nlm.nih.gov/pubmed/37676929 http://dx.doi.org/10.1177/03000605231197052 |
Sumario: | OBJECTIVE: We developed and validated a nomogram for overall survival (OS) and cancer-specific survival (CSS) prediction in patients with primary testicular diffuse large B-cell lymphoma (PT-DLBCL). METHODS: Patients diagnosed with PT-DLBCL were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were analyzed to establish a nomogram of OS and CSS. Patients were reclassified into high- and low-risk groups; survival was compared using Kaplan–Meier curves and log-rank tests. RESULTS: We collected 1099 PT-DLBCL cases (2000–2019) from SEER and randomized into training (n = 771) and validation (n = 328) cohorts. In univariate and multivariate Cox regression analyses, five prognostic indicators (age, treatment modality, diagnosis year, Ann Arbor stage, laterality) were used to establish a nomogram of OS and CSS. The nomogram demonstrated excellent discrimination and calibration, with concordance indices in the training and validation cohorts of 0.702 (95% confidence interval [CI], 0.677–0.727) and 0.705 (95% CI 0.67–0.74) for OS and 0.694 (95% CI 0.663–0.725) and 0.680 (95% CI 0.63–0.72) for CSS. The calibration curve and ROC analysis indicated good predictive capability of the nomogram. CONCLUSIONS: The constructed prognostic model showed good predictive value for PT-DLBCL to assist clinicians in developing individualized treatment strategies. |
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