ReviewClinical epidemiology2020
Statistical Challenges in Development of Prognostic Models in Diffuse Large B-Cell Lymphoma: Comparison Between Existing Models - A Systematic Review.
Review in Clinical epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Characteristics and predictive model for diffuse large B-cell lymphoma with early chemoimmunotherapy failure.Frontiers in immunology · 2025Article
- Challenges identifying DLBCL patients with poor outcomes to upfront chemoimmunotherapy and its impact on frontline clinical trials.Leukemia & lymphoma · 2024Review
- Prognostic indices in diffuse large B-cell lymphoma: a population-based comparison and validation study of multiple models.Blood cancer journal · 2023Article
- Construction and Validation of a Novel Nomogram for Predicting the Recurrence of Diffuse Large B Cell Lymphoma Treated with R-CHOP.Pharmacogenomics and personalized medicine · 2023Article
- Impact of C-reactive protein and albumin levels on short, medium, and long term mortality in patients with diffuse large B-cell lymphoma.Annals of medicine · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
BACKGROUND AND
aimBased on advances in the diagnosis, classification, and management of diffuse large B-cell lymphoma (DLBCL), a number of new prognostic models have been proposed. The aim of this study was to review and compare different prognostic models of DLBCL based on the statistical methods used to evaluate the performance of each model, as well as to analyze the possible limitations of the methods. METHODS AND
resultsA literature search identified 46 articles that proposed 55 different prognostic models for DLBCL by combining different clinical, laboratory, and other parameters of prognostic significance. In addition, six studies used nomograms, which avoid risk categorization, to create prognostic models. Only a minority of studies assessed discrimination and/or calibration to compare existing models built upon different statistical methods in the process of development of a new prognostic model. All models based on nomograms reported the c-index as a measure of discrimination. There was no uniform evaluation of the performance in other prognostic models. We compared these models of DLBCL by calculating differences and ratios of 3-year overall survival probabilities between the high- and the low-risk groups. We found that the highest and lowest ratio between low- and high-risk groups was 6 and 1.31, respectively, while the difference between these groups was 18.9% and 100%, respectively. However, these studies had limited duration of follow-up and the number of patients ranged from 71 to 335.
conclusionThere is no universal statistical instrument that could facilitate a comparison of prognostic models in DLBCL. However, when developing a prognostic model, it is recommended to report its discrimination and calibration in order to facilitate comparisons between different models. Furthermore, prognostic models based on nomograms are becoming more appealing owing to individualized disease-related risk estimations. However, they have not been validated yet in other study populations.
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