Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model

Junyi Dong; Meimei Yu; Yanwei Miao; Huicong Shen; Yi Sui; Yangyingqiu Liu; Liang Han; Xiaoxin Li; Meiying Lin; Yan Guo; Lizhi Xie

doi:10.1155/2020/5042356

Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model

Biomed Res Int. 2020 Dec 1:2020:5042356. doi: 10.1155/2020/5042356. eCollection 2020.

Authors

Junyi Dong¹, Meimei Yu², Yanwei Miao¹, Huicong Shen², Yi Sui³, Yangyingqiu Liu¹, Liang Han¹, Xiaoxin Li¹, Meiying Lin², Yan Guo⁴, Lizhi Xie⁵

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
² Department of Radiology, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100050, China.
³ Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
⁴ Life Science, GE Healthcare, Shenyang 110000, China.
⁵ GE Healthcare, MR Research China, Beijing, China.

Abstract

Background: 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.

Aim: To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM.

Method: 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.

Results: 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.

Conclusions: The radiological model based on texture features could be used to differentiate SFT/HPC from AM.

MeSH terms

Adult
Aged
Area Under Curve
Diagnosis, Differential
Female
Hemangioma / diagnostic imaging*
Hemangiopericytoma / diagnostic imaging*
Humans
Imaging, Three-Dimensional
Machine Learning
Magnetic Resonance Imaging
Male
Meningeal Neoplasms / diagnostic imaging*
Meningioma / diagnostic imaging*
Middle Aged
Prognosis
Programming Languages
ROC Curve
Reproducibility of Results
Retrospective Studies
Sensitivity and Specificity
Solitary Fibrous Tumors / diagnostic imaging*