Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Issue 7 (26th January 2014)

• Record Type: Journal Article
• Authors: Van Cauter, Sofie; De Keyzer, Frederik; Sima, Diana M.; Croitor Sava, Anca; D'Arco, Felice; Veraart, Jelle; Peeters, Ronald R.; Leemans, Alexander; Van Gool, Stefaan; Wilms, Guido; Demaerel, Philippe; Van Huffel, Sabine; Sunaert, Stefan; Himmelreich, Uwe
• Is Part Of: Neuro-oncology. Volume 16:Issue 7(2014:Jul.)
• Page Start: 1010
• Page End: 1021
• Publication Date: 2014-01-26
• Subjects: chemical shift imaging -- diffusion kurtosis imaging -- gliomas -- perfusion -- tumor grading; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/not304
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas. Methods: In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann–Whitney U -test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P -value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients. Results: Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%. Conclusions: Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.