使用放射组学分析对胶质母细胞瘤、脑转移瘤和亚型进行区分。

Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.

机构信息

Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.

Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

出版信息

J Magn Reson Imaging. 2019 Aug;50(2):519-528. doi: 10.1002/jmri.26643. Epub 2019 Jan 11.

DOI:10.1002/jmri.26643

PMID:30635952

Abstract

BACKGROUND

Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI.

PURPOSE

To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast T -weighted (T W) MRI.

STUDY TYPE

Retrospective.

SUBJECTS

Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others).

FIELD STRENGTH/SEQUENCE: Post-contrast 3D T W gradient echo images, acquired with 1.5 and 3.0 T MR systems.

ASSESSMENT

Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first- and second-order statistical, morphological, wavelet features, and bag-of-features. Following dimension reduction, classification was performed using various machine-learning algorithms including support-vector machine (SVM), k-nearest neighbor, decision trees, and ensemble classifiers.

STATISTICAL TESTS

For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.

RESULTS

For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively.

DATA CONCLUSION

Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519-528.

摘要

背景

由于治疗策略不同，区分胶质母细胞瘤和脑转移瘤非常重要。虽然 MRI 是评估脑肿瘤患者的首选方式，但由于它们在 MRI 上的表现相似，因此区分胶质母细胞瘤和单发脑转移瘤可能具有挑战性。

目的

使用基于常规对比后 T 加权（T W）MRI 的放射组学分析来区分胶质母细胞瘤和脑转移瘤亚型。

研究类型

回顾性。

受试者

数据来自 439 名患者：212 名胶质母细胞瘤患者和 227 名脑转移瘤患者（乳腺癌、肺癌和其他）。

磁场强度/序列：对比后 3D T W 梯度回波图像，在 1.5 和 3.0 T MRI 系统上采集。

评估

分析包括图像预处理、肿瘤区域分割以及特征提取，包括：患者的临床信息、肿瘤位置、一阶和二阶统计、形态学、小波特征和特征袋。在降维之后，使用各种机器学习算法（包括支持向量机（SVM）、k-最近邻、决策树和集成分类器）进行分类。

统计学检验

对于分类，数据分为训练集（80%）和测试集（20%）。在优化分类器后，计算平均灵敏度、特异性、准确性和受试者工作特征曲线下面积（AUC）。

结果

对于测试数据集，使用 SVM 分类器获得了区分胶质母细胞瘤和脑转移瘤的最佳结果，平均准确性为 0.85、敏感性为 0.86、特异性为 0.85 和 AUC 为 0.96。使用 SVM 分类器获得了区分胶质母细胞瘤和脑转移瘤亚型的最佳分类结果，平均准确性为 0.85、0.89、0.75、0.90；敏感性为 1.00、0.60、0.57、0.11；特异性为 0.76、0.92、0.87、0.99；AUC 为 0.98、0.81、0.83、0.57，分别用于胶质母细胞瘤、乳腺癌、肺癌和其他脑转移瘤。