Kawahara Daisuke, Tang Xueyan, Lee Chung K, Nagata Yasushi, Watanabe Yoichi
Department of Radiation Oncology, Institute of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.
Department of Radiation Oncology, University of Minnesota-Twin Cities, Minneapolis, MN, United States.
Front Oncol. 2021 Jan 11;10:569461. doi: 10.3389/fonc.2020.569461. eCollection 2020.
The current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.
Using MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.
By the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.
The proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.
本研究提出了一种模型，使用具有放射组学特征的机器学习（ML）方法来预测伽玛刀放射外科治疗（GKRS）的脑转移瘤（BMs）的反应。该模型可作为临床医生获得最理想治疗结果的决策工具。
使用通过FLASH（三维快速低角度激发）扫描协议采集的、带有钆（Gd）对比增强T1加权的MR图像数据，将157个转移性脑肿瘤的局部反应（LR）分为两组（第一组：反应者和第二组：无反应者）。我们对这些肿瘤进行了放射组学分析，得到了700多个特征。为了建立机器学习模型，首先，我们使用最小绝对收缩和选择算子（LASSO）回归将放射组学特征数量减少到对预测有用的最小特征数量。然后，使用具有10个隐藏层和整流线性单元激活的神经网络（NN）分类器构建预测模型。训练模型通过五折交叉验证进行评估。为了进行最终评估，将NN模型应用于一组未用于模型创建的数据。分析了LR预测模型的准确性、敏感性和受试者操作特征曲线（AUC）下的面积。将ML模型的性能与视觉评估方法进行了比较，对于视觉评估方法，通过检查MR图像上肿瘤的图像增强模式来预测肿瘤的LR。
通过对训练数据的LASSO分析，我们发现了七个对分类有用的放射组学特征。视觉评估方法的准确性和敏感性分别为44%和54%。另一方面，所提出的NN模型的准确性和敏感性分别为78%和87%，AUC为0.87。
所提出的使用放射组学特征的NN模型比传统方法能帮助医生对治疗结果有更现实的期望。
