Evidence map›Paper›PMID 30652561›Full record

ArticleJCO clinical cancer informatics2018

Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies.

Philipp M Altrock, Jeremy Ferlic, Tobias Galla, Michael H Tomasson, Franziska Michor

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Philipp M AltrockPhilipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.
Jeremy FerlicPhilipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.
Tobias GallaPhilipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.
Michael H TomassonPhilipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.
Franziska MichorPhilipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.

Funding

Viral VectorP30CA086862 · NCI · UNIVERSITY OF IOWA · PI Douglas Robert Spitz · 2000 to 2026
$70.0M
Project 3: Single Cell Measures of Intratumor Diversity for Optimal Breast Cancer TherapyU54CA193461 · NCI · DANA-FARBER CANCER INST · PI POLYAK, KORNELIA · 2015 to 2020
$11.2M
NCI NIH HHS U54 CA193461
6 · The paper itself

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.

Indexed as

Computer SimulationAdultAgedDisease ProgressionEarly Detection of CancerFemaleFloridaFollow-Up StudiesHumansMaleMiddle AgedMonoclonal Gammopathy of Undetermined SignificanceMultiple MyelomaPrecancerous ConditionsPrevalencePrognosis

Identifiers

PMID30652561
PMCPMC6873949

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.