A Competing Risk-Based Online Calculator for Cancer-Specific Survival Prediction of Patients With Spinal and Pelvic Diffuse Large B-cell Lymphoma

Study DesignRetrospective Cohort Study.ObjectivesIn this study, we aimed to evaluate the risk factors associated with cancer-specific survival (CSS) of patients with spinal and pelvic diffuse large B-cell lymphoma (SP-DLBCL) and construct a competing risk-based model for predicting 3- and 5-year CSS.MethodsData on patients diagnosed between 2010 and 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The total dataset was randomly divided into the training and testing datasets at a ratio of 7:3. Factors associated with CSS were identified by competing risk regression for the construction of a nomogram on the training dataset. Further, the testing dataset was used to validate the performance of the nomogram.ResultsCompeting risk regression revealed that age, Ann Arbor Stage, and chemotherapy were independent prognostic factors for CSS. The nomogram exhibited satisfactory agreement on calibration plots for the training and testing datasets. The nomogram performed well on the training and testing datasets, exhibiting a good clinical net benefit. Thus, a nomogram-based web calculator was constructed for clinical application.ConclusionIn this study, we evaluated the risk factors of CSS in patients with SP-DLBCL using competing risk regression analysis. The established nomogram and web-based tool performed well and could be used widely in clinical practice.

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Publication Details

Journal
Global Spine Journal
Published
2025-10-09
DOI
https://doi.org/10.1177/21925682251386849
Primary Topic
Lymphoma Diagnosis and Treatment
Type
article
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article

A Competing Risk-Based Online Calculator for Cancer-Specific Survival Prediction of Patients With Spinal and Pelvic Diffuse Large B-cell Lymphoma

Ge Jiang, Chuanfeng Wang, Jun Shang, Zhi‐Xin Yuan et al.
Global Spine Journal
Lymphoma Diagnosis and Treatment
article

A Competing Risk-Based Online Calculator for Cancer-Specific Survival Prediction of Patients With Spinal and Pelvic Diffuse Large B-cell Lymphoma

Ge Jiang, Chuanfeng Wang, Jun Shang, Zhi‐Xin Yuan, Guoxin Fan, Sheng Yang, Xiao Xiao
article en

Abstract

Study DesignRetrospective Cohort Study.ObjectivesIn this study, we aimed to evaluate the risk factors associated with cancer-specific survival (CSS) of patients with spinal and pelvic diffuse large B-cell lymphoma (SP-DLBCL) and construct a competing risk-based model for predicting 3- and 5-year CSS.MethodsData on patients diagnosed between 2010 and 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The total dataset was randomly divided into the training and testing datasets at a ratio of 7:3. Factors associated with CSS were identified by competing risk regression for the construction of a nomogram on the training dataset. Further, the testing dataset was used to validate the performance of the nomogram.ResultsCompeting risk regression revealed that age, Ann Arbor Stage, and chemotherapy were independent prognostic factors for CSS. The nomogram exhibited satisfactory agreement on calibration plots for the training and testing datasets. The nomogram performed well on the training and testing datasets, exhibiting a good clinical net benefit. Thus, a nomogram-based web calculator was constructed for clinical application.ConclusionIn this study, we evaluated the risk factors of CSS in patients with SP-DLBCL using competing risk regression analysis. The established nomogram and web-based tool performed well and could be used widely in clinical practice.

Global Spine Journal
Jiangsu University (CN), Tongji University (CN), Xuzhou Medical College (CN), Ruijin Hospital (CN), Shenzhen Sixth People's Hospital (CN), Xuzhou Cancer Hospital (CN), ShenZhen People’s Hospital (CN), Shanghai Pulmonary Hospital (CN), Shanghai East Hospital (CN), Shanghai Tenth People's Hospital (CN), Xuzhou No.1 People's Hospital (CN)
Openalex Percentile: Top 60%
Lymphoma Diagnosis and Treatment
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