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  How to predict relapse in leukemia using time series data: A comparative in silico study

Hoffmann, H., Baldow, C., Zerjatke, T., Gottschalk, A., Wagner, S., Karg, E., et al. (2021). How to predict relapse in leukemia using time series data: A comparative in silico study. PLoS One, 16(11): e0256585. doi:10.1371/journal.pone.0256585.

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 Creators:
Hoffmann, Helene1, Author
Baldow, Christoph1, Author
Zerjatke, Thomas1, Author
Gottschalk, Andrea1, Author
Wagner, Sebastian1, Author
Karg, Elena1, Author
Niehaus, Sebastian1, 2, Author
Roeder, Ingo1, 3, Author
Glauche, Ingmar1, Author
Scherf, Nico1, 4, Author              
Affiliations:
1Institute for Medical Informatics and Biometry, University Hospital Carl Gustav Carus, Dresden, Germany, ou_persistent22              
2AICURA Medical GmbH, Berlin, Germany, ou_persistent22              
3National Center of Tumor Diseases (NCT), Dresden, Germany, ou_persistent22              
4Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3282987              

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 Abstract: Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

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Language(s): eng - English
 Dates: 2021-05-212021-08-102021-11-15
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pone.0256585
Other: eCollection 2021
PMID: 34780493
PMC: PMC8592437
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Project name : -
Grant ID : 60470
Funding program : -
Funding organization : Technische Universität Dresden
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Grant ID : 031A315
Funding program : -
Funding organization : Federal Ministry of Education and Research of Germany (BMBF)

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Title: PLoS One
  Abbreviation : PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (11) Sequence Number: e0256585 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850