MRI based texture analysis to classify low grade gliomas into astrocytoma and 1p/19q codeleted oligodendroglioma
MRI based texture analysis to classify low grade gliomas into astrocytoma and 1p/19q codeleted oligodendroglioma
复制标题
DOI：
10.1016/j.mri.2018.11.008
复制
发表时间：
2019-04-01
影响因子：
2.5
通讯作者：
Kovanlikaya, Ilhami
中科院分区：
文献类型：
--
作者：
Zhang, Shun;Chiang, Gloria Chia-Yi;Kovanlikaya, Ilhami
目的：对 MR 图像进行纹理分析可以检测人类视觉评估无法察觉的定量特征。本研究的目的是评估术前常规 MRI 纹理分析区分低级别胶质瘤 (LGG) 组织学亚型的可行性，并确定纹理分析与单独直方图分析相比的效用。 方法：本研究共纳入 41 例 LGG 患者、21 例星形细胞瘤和 20 例 1p/19q 少突胶质细胞瘤患者。患者被随机分为训练组（60%）和测试组（40%）。对传统 MRI 序列进行纹理分析，以获得训练和测试数据的最具辨别力的因子 (MDF) 值。然后使用训练数据中的 MDF 值和 9 个直方图参数进行受试者工作特征 (ROC) 曲线分析，以获得用于确定测试数据中区分星形细胞瘤和少突胶质细胞瘤的正确率的截止值。结果：使用 MDF 值进行的 ROC 分析得出 T2w FLAIR 的曲线下面积 (AUC) 为 0.91（敏感性 86%，特异性 87%）， ADC 为 0.94（87%、89%），T1w 为 0.98（93%、95%），T1w + Gd 序列为 0.88（78%、86%）。使用最佳截止值，MDF 在测试数据中分别正确地区分了 94%、82%、100% 和 88% 的病例。 MDF 优于所有 9 个直方图参数。结论：对常规术前 MRI 图像进行纹理分析可以准确预测 LGG 的组织学亚型，这将对临床治疗产生影响。
Purpose: Texture analysis performed on MR images can detect quantitative features that are imperceptible to human visual assessment. The purpose of this study was to evaluate the feasibility of texture analysis on preoperative conventional MRI to discriminate between histological subtypes in low-grade gliomas (LGGs), and to determine the utility of texture analysis compared to histogram analysis alone.Methods: A total of 41 patients with LGG, 21 astrocytoma and 20 1p/19q codeleted oligodendroglioma were included in this study. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analysis was performed on conventional MRI sequences to obtain the most discriminant factor (MDF) values for both the training and testing data. Receiver operating characteristic (ROC) curve analyses were then performed using the MDF values and 9 histogram parameters in the training data to obtain cut-off values for determining the correct rate of discriminating between astrocytoma and oligodendroglioma in the testing data.Results: The ROC analyses using MDF values resulted in an area under the curve (AUC) of 0.91 (sensitivity 86%, specificity 87%) for T2w FLAIR, 0.94 (87%, 89%) for ADC, 0.98 (93%, 95%) for T1w, and 0.88 (78%, 86%) for T1w + Gd sequences. Using the best cut-off values, MDF correctly discriminated between the two groups in 94%, 82%, 100%, and 88% of cases in the testing data, respectively. The MDF outperformed all 9 of the histogram parameters.Conclusion: Texture analysis performed on conventional preoperative MRI images can accurately predict histological subtype of LGGs, which would have an impact on clinical management.