Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
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DOI：
10.3174/ajnr.a5667
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Chow D
中科院分区：
文献类型：
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作者：
Chang P;Grinband J;Weinberg BD;Bardis M;Khy M;Cadena G;Su MY;Cha S;Filippi CG;Bota D;Baldi P;Poisson LM;Jain R;Chow D
世界卫生组织最近将新的重点放在了胶质瘤遗传信息的整合上。虽然组织取样仍然是标准，但非侵入性成像技术可能会为临床相关的基因突变提供补充的洞察力。我们的目标是训练一个卷积神经网络，以高精度独立预测胶质瘤潜在的分子遗传突变状态，并确定每个突变最具预测性的成像特征。259例低级别或高级别胶质瘤患者的MR成像数据和分子信息回顾性地从癌症成像档案中获得。训练卷积神经网络对异柠檬酸脱氢酶1(IDH1)突变状态、1p/19q共缺失状态和O6-甲基鸟嘌呤-DNA甲基转移酶(MGMT)启动子甲基化状态进行分类。最后利用卷积神经网络层的主成分分析提取对分类成功至关重要的关键成像特征。分类准确率高：IDH1突变状态，94%；1p/19q共缺失，92%；MGMT启动子甲基化状态，83%。每种基因类型也与独特的影像特征相关，如肿瘤边缘的定义、T1和FLAIR抑制、水肿程度、坏死程度和质地特征。我们的结果表明，对于癌症影像档案数据集，机器学习方法可以对低级别和高级别胶质瘤的个体基因突变进行分类。结果表明，通过附加降维技术获得的相关磁共振成像特征表明，神经网络能够学习关键的成像成分，而无需事先的特征选择或人工指导的训练。
The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.