Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Weibei Dou; Mingyu Zhang; Xiaojie Zhang; Yuan Li; Hongyan Chen; Shaowu Li; Min Lu; Jianping Dai; Jean-Marc Constans

doi:10.1371/journal.pone.0137850

Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

PLoS One. 2015 Sep 14;10(9):e0137850. doi: 10.1371/journal.pone.0137850. eCollection 2015.

Authors

Weibei Dou¹, Mingyu Zhang², Xiaojie Zhang¹, Yuan Li¹, Hongyan Chen³, Shaowu Li⁴, Min Lu¹, Jianping Dai², Jean-Marc Constans⁵

Affiliations

¹ Department of Electronic Engineering, Tsinghua University, Beijing, China.
² Radiology Department of Beijing Tian Tan Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Beijing, China.
³ Radiology Department of Beijing Tian Tan Hospital, Capital Medical University, Beijing, China.
⁴ Beijing Neurosurgical Institute, Beijing, China.
⁵ Neuroradiology Service, University Hospital, Amiens, France.

Abstract

Background: Magnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.

Methods: An automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.

Results: Cho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/NAA and Cho/Cr in the AQoCE approach show a significant difference (p≤0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and Siemens MRS processing software are also discussed in this paper.

Conclusions: The AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel 1H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of Siemens.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Area Under Curve
Astrocytoma / metabolism
Astrocytoma / pathology
Automation
Brain Neoplasms / metabolism
Brain Neoplasms / pathology*
Child
Female
Glioma / metabolism
Glioma / pathology
Humans
Magnetic Resonance Imaging / methods*
Male
Middle Aged
Proton Magnetic Resonance Spectroscopy / methods*
Young Adult

Grants and funding

This work was supported by the Natural Science Foundation of China (NSFC-61171002 and NSFC-60372023) and Tsinghua University Initiative Scientific Research Program (Grant No. 20131089382). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.