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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).

Affiliations

Joongyo Lee 1 2, Sang Kyun Yoo 1, Kangpyo Kim 1 3, Byung Min Lee 1 4, Vivian Youngjean Park 5, Jin Sung Kim 1, Yong Bae Kim 1
1Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea.
2Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 06273, Republic of Korea.
3Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Yonsei University Health System, Seoul 06351, Republic of Korea.
4Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Yonsei University Health System, Uijeongbu, Gyeonggi 11765, Republic of Korea.
5Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea.