Developing a Semi-Automatised Tool for Grading Brain Tumours with Susceptibility-Weighted MRI

Abstract: Gliomas are a common type of brain tumour and for the treatment of a patient it is important to determine the tumour’s grade of malignancy. This is done today by a biopsy, a histopathological analysis of the tumourous tissue, that is classified by the World Health Organization on a malignancy scale from I to IV. Recent studies have shown that the local image variance (LIV) and the intratumoural susceptibility signal (ITSS) in susceptibility-weighted MR images correlate to the tumour grade. This thesis project aims to develop a software program as aid for the radiologists when grading a glioma. The software should by image analysis be able to separate the gliomas into low grade (I-II) and high grade (III-IV). The result is a graphical user interface written in Python 3.4.3. The user chooses an image, draws a region of interest and starts the analysis. The analyses implemented in the program are LIV and ITSS mentioned above, and the code can be extended to contain other types of analyses as research progresses. To validate the image analysis, 16 patients with glioma grades confirmed by biopsy are included in the study. Their susceptibility-weighted MR images were analysed with respect to LIV and ITSS, and the outcome of those image analyses was tested versus the known grades of the patients. No statistically significant difference could be seen between the high and the low grade group, in the case of LIV. This was probably due to hemorrhage and calcification, characteristic for some tumours and interpreted as blood vessels. Concerning ITSS a statistically significant difference could be seen between the high and the low grade group (p < 0.02). The sensitivity and specificity was 80% and 100% respec- tively. Among these 16 gliomas, 11 were astrocytic tumours and between low and high grade astrocytomas a statistically significant difference was shown. The degree of LIV was significantly different between the two groups (p < 0.03) and the sensitivity and specificity were 86% and 100% respectively. The degree of ITSS was significantly different between the two groups (p < 0.04) and the sensitivity and specificity were 86% and 100% respectively. Spearman correlation showed a correlation between LIV and tumour grade (for all gliomas r = 0.53 and p < 0.04, for astrocytomas r = 0.84 and p < 0.01). A correlation was also found between ITSS and tumour grade (for all gliomas r = 0.69 and p < 0.01, for astrocytomas r = 0.63 and p < 0.04). The results indicate that SWI is useful for distinguishing between high and low grade astrocytoma with 1.5T imaging within this cohort. It also seems possible to distinguish between high and low grade glioma with ITSS.