Lee Joongyo, Yoo Sang Kyun, Kim Kangpyo, Lee Byung Min, Park Vivian Youngjean, Kim Jin Sung, Kim Yong Bae
Department 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.
Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 06273, Republic of Korea.
Oncol Lett. 2023 Aug 11;26(4):422. doi: 10.3892/ol.2023.14008. eCollection 2023 Oct.
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).
局部区域复发（LRR）是乳腺癌根治性治疗后主要的复发模式。本研究旨在通过使用术前磁共振成像（MRI）数据，开发基于机器学习（ML）的放射组学模型来预测乳腺癌患者的LRR。收集了2013年1月至2017年12月期间接受术前MRI检查的局限性乳腺癌患者的数据。采用倾向评分匹配（PSM）来调整有和无LRR患者之间的临床因素。从有和无脂肪抑制的T2加权MRI以及有脂肪抑制的对比增强T1加权MRI中获取放射组学特征。在本研究中，设计了五个ML模型，三个基础模型（支持向量机、随机森林和逻辑回归）以及由这三个基础模型组成的两个集成模型（投票模型和堆叠模型），并将每个基础模型的性能与堆叠模型进行比较。PSM后，纳入了28例有LRR的患者和86例无LRR的患者。在这114例患者中，随机选择80例患者训练模型，其余34例患者用于评估训练后模型的性能。每位患者总共获得5064个特征，通过应用方差阈值和最小绝对收缩与选择算子选择47 - 51个特征。堆叠模型在受试者工作特征曲线下面积（AUC）方面表现出卓越性能，AUC为0.78，而其他模型的AUC范围为0.61至0.70。一项旨在研究本研究堆叠模型疗效的外部验证研究已启动且仍在进行中（韩国放射肿瘤学组2206）。
