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A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134102/ https://www.ncbi.nlm.nih.gov/pubmed/30033447 http://dx.doi.org/10.1038/s41416-018-0185-8 |
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author | Saha, Ashirbani Harowicz, Michael R. Grimm, Lars J. Kim, Connie E. Ghate, Sujata V. Walsh, Ruth Mazurowski, Maciej A. |
author_facet | Saha, Ashirbani Harowicz, Michael R. Grimm, Lars J. Kim, Connie E. Ghate, Sujata V. Walsh, Ruth Mazurowski, Maciej A. |
author_sort | Saha, Ashirbani |
collection | PubMed |
description | BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. RESULTS: Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647–0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589–0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591–0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569–0.674, p < .0001). Associations between individual features and subtypes we also found. CONCLUSIONS: There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features. |
format | Online Article Text |
id | pubmed-6134102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61341022019-09-04 A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features Saha, Ashirbani Harowicz, Michael R. Grimm, Lars J. Kim, Connie E. Ghate, Sujata V. Walsh, Ruth Mazurowski, Maciej A. Br J Cancer Article BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. RESULTS: Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647–0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589–0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591–0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569–0.674, p < .0001). Associations between individual features and subtypes we also found. CONCLUSIONS: There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features. Nature Publishing Group UK 2018-07-23 2018-08-14 /pmc/articles/PMC6134102/ /pubmed/30033447 http://dx.doi.org/10.1038/s41416-018-0185-8 Text en © Cancer Research UK 2018 https://creativecommons.org/licenses/by/4.0/Note: This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0). |
spellingShingle | Article Saha, Ashirbani Harowicz, Michael R. Grimm, Lars J. Kim, Connie E. Ghate, Sujata V. Walsh, Ruth Mazurowski, Maciej A. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features |
title | A machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 DCE-MRI features |
title_full | A machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 DCE-MRI features |
title_fullStr | A machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 DCE-MRI features |
title_full_unstemmed | A machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 DCE-MRI features |
title_short | A machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 DCE-MRI features |
title_sort | machine learning approach to radiogenomics of breast
cancer: a study of 922 subjects and 529 dce-mri features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134102/ https://www.ncbi.nlm.nih.gov/pubmed/30033447 http://dx.doi.org/10.1038/s41416-018-0185-8 |
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