ArticleJCO clinical cancer informatics2018
Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies.
Article in JCO clinical cancer informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Human preclinical multiple myeloma in vitro models for disease modeling and therapy screening.Journal of biological engineering · 2025Review
- Comparison of outcomes by race among a population-based matched sample of multiple myeloma patients.Cancer causes & control : CCC · 2025Article
- Designing optimal allocations for cancer screening using queuing network models.PLoS computational biology · 2022Article
- Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling.Cancer research · 2021Article
- System-based approaches as prognostic tools for glioblastoma.BMC cancer · 2019Review
- The 2019 mathematical oncology roadmap.Physical biology · 2019Review
- Prevention Is the Best Treatment: The Case for Understanding the Transition from Monoclonal Gammopathy of Undetermined Significance to Myeloma.International journal of molecular sciences · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeRecent advances have uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework. MATERIALS AND
methodsWe used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence, and baseline MM survival. We measured MM-specific mortality and MM prevalence after MGUS detection from simulations and mathematic modeling predictions.
resultsOur framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available point estimate of progression risk reduction to 61% risk, starting screening at age 55 years and performing follow-up screening every 6 years reduced total MM prevalence by 19%. The same reduction could be achieved with starting screening at age 65 years and performing follow-up screening every 2 years. A 40% progression risk reduction per patient with MGUS per year would reduce MM-specific mortality by 40%. Specifically, screening onset age and screening frequency can change disease prevalence, and progression risk reduction changes both prevalence and disease-specific mortality. Screening would generally be favorable in high-risk individuals.
conclusionScreening efforts should focus on specifically identified groups with high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk individuals with MGUS would require improved preventions.
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Registered trials
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