Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements

• Published in: British journal of radiology 2012-12, Vol.85 (1020), p.e1204-e1211
• Main Authors: Friedman, S N, Bambrough, P J, Kotsarini, C, Khandanpour, N, Hoggard, N
• Format: Article
• Language: English
• Subjects: Blood Volume - physiology; Blood Volume Determination - methods; Brain - blood supply; Brain Neoplasms - blood supply; Brain Neoplasms - pathology; Brain Neoplasms - physiopathology; Contrast Media; Female; Glioma - blood supply; Glioma - pathology; Glioma - physiopathology; Humans; Magnetic Resonance Angiography - methods; Male; Middle Aged; Neoplasm Grading - methods; Observer Variation; Software; Statistics as Topic
• ISSN: 0007-1285; 1748-880X
• DOI: 10.1259/bjr/13908936

Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator. MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade. A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis. Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator. Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.