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

Authors Yan LF, Sun YZ, Zhao SS, Hu YC, Han Y, Li G, Zhang X, Tian Q, Liu ZC, Yang Y, Nan HY, Yu Y, Sun Q, Zhang J, Chen P, Hu B, Li F, Han TH, Wang W, Cui GB

Received 11 December 2018

Accepted for publication 30 September 2019

Published 27 November 2019 Volume 2019:11 Pages 9989—10000


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Rituraj Purohit

Lin-Feng Yan,1,* Ying-Zhi Sun,1,* Sha-Sha Zhao,1,* Yu-Chuan Hu,1,* Yu Han,1 Gang Li,2 Xin Zhang,1 Qiang Tian,1 Zhi-Cheng Liu,1 Yang Yang,1 Hai-Yan Nan,1 Ying Yu,1 Qian Sun,1 Jin Zhang,1 Ping Chen,1 Bo Hu,1 Fei Li,3 Teng-Hui Han,3 Wen Wang,1 Guang-Bin Cui1

1Department 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; 2Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi 710038, People’s Republic of China; 3Student Brigade, Fourth Military Medical University, Xi’an, Shaanxi 710032, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wen Wang; Guang-Bin Cui
Department of Radiology & Functional and Molecular Imaging Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an 710038, People’s Republic of China
Tel +86-29-84778689; 86-29-84777863

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 Ve were demonstrated to have higher accuracies (the accuracies for Extended Tofts_Vemean and Extended Tofts_Vemedian were 68.33% and 71.67%, respectively, while those for the Incremental_Vemean and Incremental_Ve75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_Ve histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_Ktrans95th (AUC = 0.9265) and Extended Tofts_Ve95th (AUC = 0.9154) performed better than their corresponding means (Patlak_Ktransmean: AUC = 0.9118 and Extended Tofts_Vemean: 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: NCT 02622620.

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

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