Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
Department of Radiology, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100050, China.
Biomed Res Int. 2020 Dec 1;2020:5042356. doi: 10.1155/2020/5042356. eCollection 2020.
Intracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects.
To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM.
A total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model.
After Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively.
The radiological model based on texture features could be used to differentiate SFT/HPC from AM.
颅内孤立性纤维瘤/血管外皮细胞瘤（HPC）是一种起源于颅内血管的侵袭性恶性肿瘤。血管外皮细胞瘤（AM）是一种良性肿瘤，预后良好。两者的影像学表现非常相似。因此，临床上急需一种新的无创诊断方法。通过机器学习进行纹理分析和模型构建可能具有广阔的前景。
评估三维 MRI 纹理特征模型是否可用于区分颅内恶性 SFT/HPC 和 AM。
本研究纳入了 97 例 SFT/HPC 患者和 95 例 AM 患者。每组患者均随机分为训练集（70%）和测试集（30%）。使用 ITK-SNAP 软件在 T1WI、T2WI 和对比增强 T1WI 的每个层面上沿着肿瘤边缘画出 ROI。将分割后的图像导入 AK 软件进行纹理特征提取，自动获得 T1WI、T2WI 和对比增强 T1WI 的三维 ROI 信号强度直方图以及所有参数。使用 R 语言进行建模。使用混淆矩阵分析模型的准确性。构建 ROC 曲线评估逻辑回归模型的分级能力。
经过 Lasso 降维，从 T1WI、T2WI 和对比增强 T1WI 中分别提取了 5、9 和 7 个纹理特征；组合序列中还提取了 8 个纹理特征用于建模。对四个模型的 ROC 分析得出，T1WI 模型的曲线下面积（AUC）为 0.885（敏感性 76.1%，特异性 87.9%），T2WI 模型为 0.918（敏感性 73.1%，特异性 95.5%），对比增强 T1WI 模型为 0.815（敏感性 55.2%，特异性 93.9%），组合序列模型为 0.959（敏感性 92.5%，特异性 84.8%），在测试集中分别足以正确区分两组中的 71.2%、81.4%、69.5%和 83.1%的病例。
基于纹理特征的放射影像学模型可用于区分 SFT/HPC 和 AM。
