Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Lin-Feng Yan; Ying-Zhi Sun; Sha-Sha Zhao; Yu-Chuan Hu; Yu Han; Gang Li; Xin Zhang; Qiang Tian; Zhi-Cheng Liu; Yang Yang; Hai-Yan Nan; Ying Yu; Qian Sun; Jin Zhang; Ping Chen; Bo Hu; Fei Li; Teng-Hui Han; Wen Wang; Guang-Bin Cui

doi:10.2147/CMAR.S197839

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Cancer Manag Res. 2019 Nov 27:11:9989-10000. doi: 10.2147/CMAR.S197839. eCollection 2019.

Authors

Lin-Feng Yan^#¹, Ying-Zhi Sun^#¹, Sha-Sha Zhao^#¹, Yu-Chuan Hu^#¹, Yu Han¹, Gang Li², Xin Zhang¹, Qiang Tian¹, Zhi-Cheng Liu¹, Yang Yang¹, Hai-Yan Nan¹, Ying Yu¹, Qian Sun¹, Jin Zhang¹, Ping Chen¹, Bo Hu¹, Fei Li³, Teng-Hui Han³, Wen Wang¹, Guang-Bin Cui¹

Affiliations

¹ Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China.
² Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China.
³ Student Brigade, Fourth Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients' survival.

Patients and methods: A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients' survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan-Meier curve was utilized to predict patients' survival.

Results: Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of V_e were demonstrated to have higher accuracies (the accuracies for Extended Tofts_V_e ^mean and Extended Tofts_V_e ^median were 68.33% and 71.67%, respectively, while those for the Incremental_V_e ^mean and Incremental_V_e ^75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_V_e histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_K_trans ^95th (AUC = 0.9265) and Extended Tofts_V_e ^95th (AUC = 0.9154) performed better than their corresponding means (Patlak_K_trans ^mean: AUC = 0.9118 and Extended Tofts_V_e ^mean: AUC = 0.9044) in predicting patients' overall survival (OS) at 18-month follow-up.

Conclusion: DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading.

Trial registration: NCT02622620.

Keywords: glioma grading; histogram features; multi-modal MRI; receiver operating curve; survival analysis.

Associated data

ClinicalTrials.gov/NCT02622620

Grants and funding

This study was registered to ClinicalTrials.gov (NCT02622620, https://www.clinicaltrials.gov/) and received financial support from the National Key Research and Development Program of China [No. 2016YFC0107105], Science and Technology Development of Shaanxi Province [No. 2014JZ2-007; 2015KW-039] and Innovation and Development Foundation of Tangdu Hospital [No. 2016LCYJ001].