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Using machine learning to predict individual patient toxicities from cancer treatments
PURPOSE: Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS). METHODS: A gradient boosted d...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385785/ https://www.ncbi.nlm.nih.gov/pubmed/35614153 http://dx.doi.org/10.1007/s00520-022-07156-6 |
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author | Cole, Katherine Marie Clemons, Mark McGee, Sharon Alzahrani, Mashari Larocque, Gail MacDonald, Fiona Liu, Michelle Pond, Gregory R. Mosquera, Lucy Vandermeer, Lisa Hutton, Brian Piper, Ardelle Fernandes, Ricardo Emam, Khaled El |
author_facet | Cole, Katherine Marie Clemons, Mark McGee, Sharon Alzahrani, Mashari Larocque, Gail MacDonald, Fiona Liu, Michelle Pond, Gregory R. Mosquera, Lucy Vandermeer, Lisa Hutton, Brian Piper, Ardelle Fernandes, Ricardo Emam, Khaled El |
author_sort | Cole, Katherine Marie |
collection | PubMed |
description | PURPOSE: Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS). METHODS: A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependence plots were constructed to illustrate relationships between variables and the outcome of interest. RESULTS: The model area under the ROC curve was 0.731 (SD 0.074). The most important variables in the model were as follows: the number of hot flashes per week, age, the prescription, or use of drug interventions to manage VMS, whether patients were asked about VMS in routine follow-up visits, and the presence or absence of changes to breast cancer treatments due to VMS. A threshold of 17 hot flashes per week was identified as being more predictive of severe VMS. Patients between the ages of 49 and 63 were more likely to report severe symptoms. CONCLUSION: Machine learning is a unique tool for predicting severe VMS. The use of ML to assess other treatment-related toxicities and their management requires further study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00520-022-07156-6. |
format | Online Article Text |
id | pubmed-9385785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-93857852022-08-19 Using machine learning to predict individual patient toxicities from cancer treatments Cole, Katherine Marie Clemons, Mark McGee, Sharon Alzahrani, Mashari Larocque, Gail MacDonald, Fiona Liu, Michelle Pond, Gregory R. Mosquera, Lucy Vandermeer, Lisa Hutton, Brian Piper, Ardelle Fernandes, Ricardo Emam, Khaled El Support Care Cancer Original Article PURPOSE: Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS). METHODS: A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependence plots were constructed to illustrate relationships between variables and the outcome of interest. RESULTS: The model area under the ROC curve was 0.731 (SD 0.074). The most important variables in the model were as follows: the number of hot flashes per week, age, the prescription, or use of drug interventions to manage VMS, whether patients were asked about VMS in routine follow-up visits, and the presence or absence of changes to breast cancer treatments due to VMS. A threshold of 17 hot flashes per week was identified as being more predictive of severe VMS. Patients between the ages of 49 and 63 were more likely to report severe symptoms. CONCLUSION: Machine learning is a unique tool for predicting severe VMS. The use of ML to assess other treatment-related toxicities and their management requires further study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00520-022-07156-6. Springer Berlin Heidelberg 2022-05-25 2022 /pmc/articles/PMC9385785/ /pubmed/35614153 http://dx.doi.org/10.1007/s00520-022-07156-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Cole, Katherine Marie Clemons, Mark McGee, Sharon Alzahrani, Mashari Larocque, Gail MacDonald, Fiona Liu, Michelle Pond, Gregory R. Mosquera, Lucy Vandermeer, Lisa Hutton, Brian Piper, Ardelle Fernandes, Ricardo Emam, Khaled El Using machine learning to predict individual patient toxicities from cancer treatments |
title | Using machine learning to predict individual patient toxicities from cancer treatments |
title_full | Using machine learning to predict individual patient toxicities from cancer treatments |
title_fullStr | Using machine learning to predict individual patient toxicities from cancer treatments |
title_full_unstemmed | Using machine learning to predict individual patient toxicities from cancer treatments |
title_short | Using machine learning to predict individual patient toxicities from cancer treatments |
title_sort | using machine learning to predict individual patient toxicities from cancer treatments |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385785/ https://www.ncbi.nlm.nih.gov/pubmed/35614153 http://dx.doi.org/10.1007/s00520-022-07156-6 |
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