SU-E-J-139: Fuzzy Clustering Segmentation of Glioblastoma in T1-MRI Imaging for Clinical Trials

Purpose: Generating brain tumor volume measurements in a reproducible and efficient manner is a difficult, yet necessary, component of response assessment. The purpose of this study was to adapt and validate a multilevel Fuzzy C-means clustering algorithms for ROI tumor segmentation to allow consistent volumetric comparisons at multiple sites. Methods: Preoperative contrast-enhanced T1W images from 37 glioblastoma cases were segmented using Fuzzy C-means clustering-based methods and compared to manually contoured volumes created by specialists. The same was done post-operatively, using subtracted images to eliminate intrinsically T1-hyperintense material (blood). Volume computations based on the MacDonald criteria were also used for comparison. Agreement and inter-rater variability between volumes produced with each method was assessed by determining the concordance correlation coefficient (CCC). Results: The MacDonald criteria method had poor agreement (CCC=0.350–0.972) with manual contouring pre- and postoperatively, while the proposed semi-automated methods exhibited very high agreement (CCC=0.839–0.995) with manual contouring before and after resection. Fuzzy C-means clustering with three classes was the most robust semi-automated method, showing better inter-rater agreement than the MacDonald criteria method for both pre- (CCC of 0.990 and 0.975, respectively) and post-operative cases (CCC of 0.983 and 0.576, respectively). Post-operative inter-rater agreement was significantly different between these methods (p < 0.001). Conclusion: The proposed semi-automated segmentation methods allow tumor volume measurements of MR images in a reliable and reproducible fashion necessary for measuring treatment response in glioblastoma patients in multicenter clinical trials.