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

Release time:2021-12-25  Hits:

• Impact Factor:3.24
• DOI number:10.1371/journal.pone.0137850
• Journal:PLOS ONE
• Place of Publication:UNITED STATES
• Key Words:MAGNETIC-RESONANCE-SPECTROSCOPYPROTON MR SPECTROSCOPYCEREBRAL GLIOMA GRADENONINVASIVE EVALUATIONPREDICTIVE VALUESDIFFUSIONTIMEQUANTIFICATIONSPECIFICITYSENSITIVITY
• 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 H-1-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.
• Co-author:Mingyu Zhang, Xiaojie Zhang, Yuan Li, Hongyan chen, Shaowu Li, Min Lu, Jianping Dai, Jean-Marc Constans,Xue Feng,Boxun Li,Yu WANG,Kaiyu CUI,Fang LIU, Weibei Dou,Yidong HUANG
• First Author:Weibei Dou,Hong Zhang
• Indexed by:Journal paper
• Correspondence Author:Weibei Dou,Xue Feng
• Volume:10
• Issue:9
• ISSN No.:1932-6203
• Translation or Not:no
• Date of Publication:2015-09-14