Department of Radiology, Peking University First Hospital, Beijing, China.
Breast Disease Center, Peking University First Hospital, Beijing, China.
Eur J Radiol. 2022 Jan;146:110095. doi: 10.1016/j.ejrad.2021.110095. Epub 2021 Dec 4.
To establish radiomics prediction models based on automatic segmented magnetic resonance imaging (MRI) for predicting the systemic recurrence of triple-negative breast cancer (TNBC) in patients after neoadjuvant chemotherapy (NAC).
A total of 147 patients with TNBC who underwent NAC between January 2009 and December 2018 were enrolled in this study. Clinicopathologic data were collected, and the differences between the recurrent and nonrecurrent patients were analyzed by univariate and multivariate analyses. Patients were randomly divided into training and testing sets. The training set consisted of 104 patients (recurrence: 22, nonrecurrence: 82), and the testing set consisted of 43 patients (recurrence: 9, nonrecurrence: 34). To establish the radiomics prediction model, we used a deep learning segmentation model to automatically segment tumor areas on dynamiccontrast-enhanced-MRI images of pre- and post-NAC magnetic resonance examinations. Radiomics features were then extracted from the tumor areas. Three MRI radiomics models were developed in the training set: a radiomics model based on pre-NAC MRI features (model 1), a radiomics model based on post-NAC MRI features (model 2), and a radiomics model based on both pre- and post-NAC MRI features (model 3). A clinical model for predicting systemic recurrence was built in the training set using independent clinical prediction factors. Receiver operating characteristic curve analysis was used to evaluate the performance of the radiomics and clinical models.
The clinical model yielded areas under the curve (AUCs) of 0.747 in the training set and 0.737 in the testing set in terms of predicting systemic recurrence. Models 1, 2, and 3 yielded AUCs of 0.879, 0.91, and 0.963 in the training set and 0.814, 0.802, and 0.933 in the testing set, respectively, in terms of predicting systemic recurrence. All of the radiomics models had achieved higher AUCs than the clinical model in the testing set. DeLong test was used to compare the AUCs between the models and indicated that the predictive performance of model 3 was better than the clinical model, and the difference was statistically significant (p < 0.05).
The radiomics models built based on the combination of pre- and post-NAC MRI features showed good performance in predicting whether patients with TNBC will have systemic recurrence within 3 years post-NAC. This can help us non-invasively identify which patients are at high risk of recurrence post-NAC, so that we can strengthen follow-up and treatment of these patients. Then the prognosis of these patients might be improved.
建立基于自动分割磁共振成像（MRI）的放射组学预测模型，以预测接受新辅助化疗（NAC）后三阴性乳腺癌（TNBC）患者的全身复发情况。
本研究纳入了 2009 年 1 月至 2018 年 12 月期间接受 NAC 的 147 例 TNBC 患者。收集了临床病理数据，并通过单因素和多因素分析比较了复发组和非复发组患者之间的差异。患者被随机分为训练集和测试集。训练集包括 104 例患者（复发：22 例，非复发：82 例），测试集包括 43 例患者（复发：9 例，非复发：34 例）。为了建立放射组学预测模型，我们使用深度学习分割模型自动分割 NAC 前后磁共振检查的动态对比增强 MRI 图像中的肿瘤区域。然后从肿瘤区域提取放射组学特征。在训练集中建立了三个 MRI 放射组学模型：基于 NAC 前 MRI 特征的放射组学模型（模型 1）、基于 NAC 后 MRI 特征的放射组学模型（模型 2）和基于 NAC 前后 MRI 特征的放射组学模型（模型 3）。在训练集中使用独立的临床预测因素建立了用于预测全身复发的临床模型。使用受试者工作特征曲线分析评估放射组学和临床模型的性能。
临床模型在训练集和测试集的预测全身复发方面的曲线下面积（AUC）分别为 0.747 和 0.737。模型 1、2 和 3 在训练集和测试集的预测全身复发方面的 AUC 分别为 0.879、0.91 和 0.963。在测试集中，所有放射组学模型的 AUC 均高于临床模型。使用 DeLong 检验比较了模型之间的 AUC，结果表明模型 3 的预测性能优于临床模型，差异具有统计学意义（p<0.05）。
基于 NAC 前后 MRI 特征相结合建立的放射组学模型在预测 TNBC 患者 NAC 后 3 年内是否会发生全身复发方面表现出良好的性能。这有助于我们无创地识别出哪些患者在 NAC 后有较高的复发风险，从而加强对这些患者的随访和治疗，进而改善这些患者的预后。
