深度学习在磁共振成像上对脑膜瘤组织病理学分级的鉴别准确性：一项初步研究。
Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.
机构信息
Department of Animal Medicine, Productions and Health, University of Padua, Legnaro, Italy.
Neuroradiology Unit, Padua University Hospital, Padova, Italy.
出版信息
J Magn Reson Imaging. 2019 Oct;50(4):1152-1159. doi: 10.1002/jmri.26723. Epub 2019 Mar 21.
BACKGROUND
Grading of meningiomas is important in the choice of the most effective treatment for each patient.
PURPOSE
To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images.
STUDY TYPE
Retrospective.
POPULATION
In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III.
FIELD STRENGTH/SEQUENCE: 1.5 T, 3.0 T postcontrast enhanced T W (PCT W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm ).
ASSESSMENT
WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance.
STATISTICAL TEST
Leave-one-out cross-validation.
RESULTS
The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T W images was low, with Inception-V3 displaying an AUC of 0.68 (95% CI, 0.59-0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45-0.64).
DATA CONCLUSION
DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT W images.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159.
背景
在为每位患者选择最有效的治疗方法时，对脑膜瘤进行分级很重要。
目的
确定深度卷积神经网络（DCNN）在从磁共振图像中区分脑膜瘤的组织病理学分级方面的诊断准确性。
研究类型
回顾性。
人群
共有 117 名脑膜瘤患者，79 名世界卫生组织（WHO）I 级，32 名 WHO II 级，6 名 WHO III 级。
磁场强度/序列：1.5T，3.0T 对比增强 T W（PCT W），表观扩散系数（ADC）图（b 值为 0、500 和 1000 s/mm ）。
评估
将 WHO II 级和 WHO III 级脑膜瘤视为单一类别。分别在 ADC 图和 PCT W 图像上测试预先训练的 Inception-V3 和 AlexNet DCNN 的诊断准确性。使用受试者工作特征曲线（ROC）和曲线下面积（AUC）评估 DCNN 性能。
统计检验
留一交叉验证。
结果
在 ADC 图上应用 Inception-V3 DCNN 提供了最佳的诊断准确性结果，AUC 为 0.94（95%置信区间[CI]，0.88-0.98）。值得注意的是，只有 1/38 的 WHO II-III 级和 7/79 的 WHO I 级病变被该模型错误分类。AlexNet 在 ADC 图上的应用具有较低的鉴别准确性，AUC 为 0.68（95%CI，0.59-0.76），对 WHO I 级和 WHO II-III 级病例的分类错误率较高。两种 DCNN 在对比后 T W 图像上的鉴别准确性均较低，Inception-V3 的 AUC 为 0.68（95%CI，0.59-0.76），AlexNet 的 AUC 为 0.55（95%CI，0.45-0.64）。
数据结论
DCNN 可以从 ADC 图中准确区分良性和非典型/间变性脑膜瘤，但不能从 PCT W 图像中区分。
证据水平
2 技术功效：第 2 阶段 J. Magn. Reson. Imaging 2019;50:1152-1159。
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