Journal article
Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies
JCO clinical cancer informatics, Vol.2(2), pp.1-12
03/22/2018
DOI: 10.1200/CCI.17.00131
PMCID: PMC6873949
PMID: 30652561
Abstract
Purpose Recent 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 Methods We 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.
Results Our 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, staffing 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.
Conclusion Screening 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. (C) 2018 by American Society of Clinical Oncology
Details
- Title: Subtitle
- Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies
- Creators
- Philipp M. Altrock - Moffitt Cancer CenterJeremy Ferlic - University of South FloridaTobias Galla - University of ManchesterMichael H. Tomasson - Harvard UniversityFranziska Michor - Dana-Farber Cancer Institute
- Resource Type
- Journal article
- Publication Details
- JCO clinical cancer informatics, Vol.2(2), pp.1-12
- DOI
- 10.1200/CCI.17.00131
- PMID
- 30652561
- PMCID
- PMC6873949
- NLM abbreviation
- JCO Clin Cancer Inform
- ISSN
- 2473-4276
- eISSN
- 2473-4276
- Publisher
- Amer Soc Clinical Oncology
- Number of pages
- 12
- Grant note
- LPDS 2012-12 / Deutsche Akademie der Naturforscher Leopoldina EP/K037145/1 / Engineering and Physical Sciences Research Council; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC) Moffitt Cancer Center and Research Institute U54CA193461 / National Cancer Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI) U54CA193461; P30CA086862 / NATIONAL CANCER INSTITUTE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
- Language
- English
- Date published
- 03/22/2018
- Academic Unit
- Hematology, Oncology, and Blood & Marrow Transplantation; Health, Sport, and Human Physiology ; Internal Medicine
- Record Identifier
- 9984360153202771
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