The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest
The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest
复制标题
基于ADC图纹理分析预测脑膜瘤WHO分级的诊断价值：决策树和决策森林的尝试
DOI：
10.1007/s00330-018-5632-7
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发表时间：
2019-03-01
影响因子：
5.9
通讯作者：
Yin, Bo
中科院分区：
文献类型：
--
作者：
Lu, Yiping;Liu, Li;Yin, Bo
目的术前预测脑膜瘤的WHO分级对制定进一步的治疗计划具有重要意义。本研究的目的是评估是否纹理分析（TA）的基础上表观扩散系数（ADC）地图可以非侵入性分类脑膜瘤准确使用树classifiers.MethodsA病理数据库进行了审查，以确定脑膜瘤患者在我院进行肿瘤切除术与术前常规MRI扫描和扩散加权成像（DWI）2011年1月至2017年8月。共纳入152例脑膜瘤患者的421个术前ADC图。提取了四类特征，即临床特征、形态特征、平均ADC值和纹理特征。三个机器学习分类器，即经典的决策树，条件推理树和决策森林，建立在这些功能的训练数据集。然后，每个分类器的性能进行了评估，并与两个神经放射科医生的诊断进行比较。ResultsThe ADC值单独无法区分三个WHO等级的脑膜瘤。基于临床、形态学特征和ADC值的机器学习分类器可以实现与两名经验丰富的神经放射科医生（准确率= 61.11%和62.04%）相当的诊断性能（准确率= 62.96%）。经过分析，由23个选择的纹理特征和训练数据集的ADC值构建的决策森林在测试数据集中实现了最佳的诊断性能（kappa = 0.64，准确率= 79.51%）。结论决策森林与ADC值和ADC图-基于纹理特征的多类分类器是一种很有前途的多类分类器，在不久的将来可能提供更精确的诊断和辅助诊断。dot精确的脑膜瘤WHO分级术前预测为进一步治疗带来益处plans.center dot基于临床、形态学特征和ADC值的机器学习模型与经验丰富的模型相比，可以实现同等的诊断性能neuroradiologists.center dot使用23个选定的纹理特征和ADC值构建的决策森林模型实现了最佳的诊断性能（kappa = 0.64，准确率= 79.51%）。
ObjectivesThe preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classify meningiomas accurately using tree classifiers.MethodsA pathology database was reviewed to identify meningioma patients who underwent tumour resection in our hospital with preoperative routine MRI scanning and diffusion-weighted imaging (DWI) between January 2011 and August 2017. A total of 152 meningioma patients with 421 preoperative ADC maps were included. Four categories of features, namely, clinical features, morphological features, average ADC values and texture features, were extracted. Three machine learning classifiers, namely, classic decision tree, conditional inference tree and decision forest, were built on these features from the training dataset. Then the performance of each classifier was evaluated and compared with the diagnosis made by two neuro-radiologists.ResultsThe ADC value alone was unable to distinguish three WHO grades of meningiomas. The machine learning classifiers based on clinical, morphological features and ADC value could achieve equivalent diagnostic performance (accuracy = 62.96%) compared to two experienced neuro-radiologists (accuracy = 61.11% and 62.04%). Upon analysis, the decision forest that was built with 23 selected texture features and the ADC value from the training dataset achieved the best diagnostic performance in the testing dataset (kappa = 0.64, accuracy = 79.51%).ConclusionsDecision forest with the ADC value and ADC map-based texture features is a promising multiclass classifier that could potentially provide more precise diagnosis and aid diagnosis in the near future.Key Points center dot A precise preoperative prediction of the WHO grade of a meningioma brings benefits to further treatment plans.center dot Machine learning models based on clinical, morphological features and ADC value could achieve equivalent diagnostic performance compared to experienced neuroradiologists.center dot The decision forest model built with 23 selected texture features and the ADC value achieved the best diagnostic performance (kappa = 0.64, accuracy = 79.51%).