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Predicting regrowth of low-grade gliomas after radiotherapy
Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To imp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10128962/ https://www.ncbi.nlm.nih.gov/pubmed/37000852 http://dx.doi.org/10.1371/journal.pcbi.1011002 |
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author | Plaszczynski, Stéphane Grammaticos, Basile Pallud, Johan Campagne, Jean-Eric Badoual, Mathilde |
author_facet | Plaszczynski, Stéphane Grammaticos, Basile Pallud, Johan Campagne, Jean-Eric Badoual, Mathilde |
author_sort | Plaszczynski, Stéphane |
collection | PubMed |
description | Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient’s health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all “fast-responders”. The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT. |
format | Online Article Text |
id | pubmed-10128962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101289622023-04-26 Predicting regrowth of low-grade gliomas after radiotherapy Plaszczynski, Stéphane Grammaticos, Basile Pallud, Johan Campagne, Jean-Eric Badoual, Mathilde PLoS Comput Biol Research Article Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient’s health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all “fast-responders”. The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT. Public Library of Science 2023-03-31 /pmc/articles/PMC10128962/ /pubmed/37000852 http://dx.doi.org/10.1371/journal.pcbi.1011002 Text en © 2023 Plaszczynski et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Plaszczynski, Stéphane Grammaticos, Basile Pallud, Johan Campagne, Jean-Eric Badoual, Mathilde Predicting regrowth of low-grade gliomas after radiotherapy |
title | Predicting regrowth of low-grade gliomas after radiotherapy |
title_full | Predicting regrowth of low-grade gliomas after radiotherapy |
title_fullStr | Predicting regrowth of low-grade gliomas after radiotherapy |
title_full_unstemmed | Predicting regrowth of low-grade gliomas after radiotherapy |
title_short | Predicting regrowth of low-grade gliomas after radiotherapy |
title_sort | predicting regrowth of low-grade gliomas after radiotherapy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10128962/ https://www.ncbi.nlm.nih.gov/pubmed/37000852 http://dx.doi.org/10.1371/journal.pcbi.1011002 |
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