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Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer
Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)-based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Dat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10472028/ https://www.ncbi.nlm.nih.gov/pubmed/37664669 http://dx.doi.org/10.3892/ol.2023.14008 |
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author | Lee, Joongyo Yoo, Sang Kyun Kim, Kangpyo Lee, Byung Min Park, Vivian Youngjean Kim, Jin Sung Kim, Yong Bae |
author_facet | Lee, Joongyo Yoo, Sang Kyun Kim, Kangpyo Lee, Byung Min Park, Vivian Youngjean Kim, Jin Sung Kim, Yong Bae |
author_sort | Lee, Joongyo |
collection | PubMed |
description | Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)-based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Data from patients with localized breast cancer that underwent preoperative MRI between January 2013 and December 2017 were collected. Propensity score matching (PSM) was performed to adjust for clinical factors between patients with and without LRR. Radiomics features were obtained from T2-weighted with and without fat-suppressed MRI and contrast-enhanced T1-weighted with fat-suppressed MRI. In the present study five ML models were designed, three base models (support vector machine, random forest, and logistic regression) and two ensemble models (voting model and stacking model) composed of the three base models, and the performance of each base model was compared with the stacking model. After PSM, 28 patients with LRR and 86 patients without LRR were included. Of these 114 patients, 80 patients were randomly selected to train the models, and the remaining 34 patients were used to evaluate the performance of the trained models. In total, 5,064 features were obtained from each patient, and 47–51 features were selected by applying variance threshold and least absolute shrinkage and selection operator. The stacking model demonstrated superior performance in area under the receiver operating characteristic curve (AUC), with an AUC of 0.78 compared to a range of 0.61 to 0.70 for the other models. An external validation study to investigate the efficacy of the stacking model of the present study was initiated and is still ongoing (Korean Radiation Oncology Group 2206). |
format | Online Article Text |
id | pubmed-10472028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-104720282023-09-02 Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer Lee, Joongyo Yoo, Sang Kyun Kim, Kangpyo Lee, Byung Min Park, Vivian Youngjean Kim, Jin Sung Kim, Yong Bae Oncol Lett Articles Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)-based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Data from patients with localized breast cancer that underwent preoperative MRI between January 2013 and December 2017 were collected. Propensity score matching (PSM) was performed to adjust for clinical factors between patients with and without LRR. Radiomics features were obtained from T2-weighted with and without fat-suppressed MRI and contrast-enhanced T1-weighted with fat-suppressed MRI. In the present study five ML models were designed, three base models (support vector machine, random forest, and logistic regression) and two ensemble models (voting model and stacking model) composed of the three base models, and the performance of each base model was compared with the stacking model. After PSM, 28 patients with LRR and 86 patients without LRR were included. Of these 114 patients, 80 patients were randomly selected to train the models, and the remaining 34 patients were used to evaluate the performance of the trained models. In total, 5,064 features were obtained from each patient, and 47–51 features were selected by applying variance threshold and least absolute shrinkage and selection operator. The stacking model demonstrated superior performance in area under the receiver operating characteristic curve (AUC), with an AUC of 0.78 compared to a range of 0.61 to 0.70 for the other models. An external validation study to investigate the efficacy of the stacking model of the present study was initiated and is still ongoing (Korean Radiation Oncology Group 2206). D.A. Spandidos 2023-08-11 /pmc/articles/PMC10472028/ /pubmed/37664669 http://dx.doi.org/10.3892/ol.2023.14008 Text en Copyright: © Lee et al. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Lee, Joongyo Yoo, Sang Kyun Kim, Kangpyo Lee, Byung Min Park, Vivian Youngjean Kim, Jin Sung Kim, Yong Bae Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title | Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title_full | Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title_fullStr | Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title_full_unstemmed | Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title_short | Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
title_sort | machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10472028/ https://www.ncbi.nlm.nih.gov/pubmed/37664669 http://dx.doi.org/10.3892/ol.2023.14008 |
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