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Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer

Authors
 Joongyo Lee  ;  Sang Kyun Yoo  ;  Kangpyo Kim  ;  Byung Min Lee  ;  Vivian Youngjean Park  ;  Jin Sung Kim  ;  Yong Bae Kim 
Citation
 ONCOLOGY LETTERS, Vol.26(4) : 422, 2023-08 
Journal Title
ONCOLOGY LETTERS
ISSN
 1792-1074 
Issue Date
2023-08
Abstract
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).
Files in This Item:
T202304670.pdf Download
DOI
10.3892/ol.2023.14008
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Kim, Yong Bae(김용배) ORCID logo https://orcid.org/0000-0001-7573-6862
Kim, Jinsung(김진성) ORCID logo https://orcid.org/0000-0003-1415-6471
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Lee, Byung Min(이병민)
Lee, Joongyo(이준교)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196216
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