Machine Learning in Preoperative Glioma MRI: Survival Associations by Perfusion-Based Support Vector Machine Outperforms Traditional MRI
Machine Learning in Preoperative Glioma MRI: Survival Associations by Perfusion-Based Support Vector Machine Outperforms Traditional MRI
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DOI：
10.1002/jmri.24390
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发表时间：
2014-07-01
影响因子：
4.4
通讯作者：
Zoellner, Frank G.
中科院分区：
文献类型：
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作者：
Emblem, Kyrre E.;Due-Tonnessen, Paulina;Zoellner, Frank G.
目的：回顾性评价自动支持向量机(SVM)常规结合基于灌注的动态磁化率对比磁共振成像(DSC-MRI)在脑胶质瘤患者术前生存关联中的作用，并将我们的结果与传统MRI进行比较。材料和方法：该研究获得伦理委员会批准，并签署了知情同意书。94例成年患者(男性49例，女性45例，年龄23-82岁，平均51岁)在手术前接受了1.5-T的结构、弥散和灌注磁共振成像，这些患者后来被诊断为原发胶质瘤。患者被随机分配到训练和测试数据集中，通过支持向量机得到的基于DSC的生存关联与传统的MRI特征进行比较，包括造影剂增强、灌注和扩散加权成像、肿瘤大小和位置。结果：在测试数据集中的1-(26/33活着，11/14死亡)、2-(15/21，21/26)、3-(12/16，27/31)和4(12/15，28/32)年生存关联中，支持向量机常规是唯一与生存一致相关的生物标志物(COX；P<；0.001)。结论：本研究中提出的自动机器学习程序可能为操作员提供一种可靠的工具来评估胶质瘤患者的生存。
Purpose: To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion-based dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI.Materials and Methods: The study was approved by the Ethics Committee and informed consent was signed. Structural, diffusion-and perfusion-weighted MRI was performed at 1.5-T preoperatively in 94 adult patients (49 males, 45 females, 23-82 years; mean 51 years) later diagnosed with a primary glioma. Patients were randomly assigned in training and test datasets and the resulting DSC-based survival associations by SVM were compared to traditional MRI features including contrast-agent enhancement, perfusion-and diffusion-weighted imaging, tumor size, and location. The results were adjusted for age, neurological status, and postoperative factors associated with survival, including surgery and adjuvant therapy.Results: For 1- (26/33 alive, 11/14 deceased), 2- (15/21, 21/26), 3- (12/16, 27/31) and 4- (12/15, 28/32) year survival associations in the test dataset (47 patients), the SVM routine was the only biomarker to consistently associate with survival (Cox; P < 0.001).Conclusion: The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.