Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers
Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers
复制标题
使用周围增强水肿区域衍生的放射学特征和多个分类器区分幕上单脑转移瘤和胶质母细胞瘤
DOI：
10.1007/s00330-019-06460-w
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发表时间：
2020-01-31
影响因子：
5.9
通讯作者：
Zhang, Minming
中科院分区：
文献类型：
--
作者：
Dong, Fei;Li, Qian;Zhang, Minming
目的应用脑水肿强化区放射组学特征及多分类器鉴别幕上单发脑转移瘤（MET）和胶质母细胞瘤（GBM）。方法回顾性分析120例单脑MET和GBM，随机分为训练数据集（70%）和验证数据集（30%）。从常规MR图像的周围增强水肿区域提取每个病例的定量放射学特征。在特征选择之后，建立了五个分类器。此外，研究了分类器的组合使用。准确性，灵敏度和特异性被用来评估分类性能。结果共提取321个特征，每个病例选取3个特征。5个分类器显示训练数据集的准确度为0.70至0.76，灵敏度为0.57至0.98，特异性为0.43至0.93，验证数据集的准确度为0.56至0.64，灵敏度为0.39至0.78，特异性为0.50至0.89。在组合分类器时，分类器的组合方式和一致性模式不同，分类器的分类性能也不同，当所有分类器采用相同的权重和简单多数表决法达到一致时，分类器的分类性能最好。结论增强区周围水肿区的3个特征对幕上单发脑MET与GBM的5个单分类器鉴别诊断具有中等价值。分类器的组合使用，如多学科团队（MDT）咨询，可以带来额外的好处，特别是对于所有分类器达成一致的情况。
Objective To differentiate supratentorial single brain metastasis (MET) from glioblastoma (GBM) by using radiomic features derived from the peri-enhancing oedema region and multiple classifiers. Methods One hundred and twenty single brain METs and GBMs were retrospectively reviewed and then randomly divided into a training data set (70%) and validation data set (30%). Quantitative radiomic features of each case were extracted from the peri-enhancing oedema region of conventional MR images. After feature selection, five classifiers were built. Additionally, the combined use of the classifiers was studied. Accuracy, sensitivity, and specificity were used to evaluate the classification performance. Results A total of 321 features were extracted, and 3 features were selected for each case. The 5 classifiers showed an accuracy of 0.70 to 0.76, sensitivity of 0.57 to 0.98, and specificity of 0.43 to 0.93 for the training data set, with an accuracy of 0.56 to 0.64, sensitivity of 0.39 to 0.78, and specificity of 0.50 to 0.89 for the validation data set. When combining the classifiers, the classification performance differed according to the combined mode and the agreement pattern of classifiers, and the greatest benefit was obtained when all the classifiers reached agreement using the same weight and simple majority vote method. Conclusions Three features derived from the peri-enhancing oedema region had moderate value in differentiating supratentorial single brain MET from GBM with five single classifiers. Combined use of classifiers, like multi-disciplinary team (MDT) consultation, could confer extra benefits, especially for those cases when all classifiers reach agreement.