Diagnosis of neuroendocrine tumours by retrospective image fusion: is there a benefit?
This study evaluated the use of image fusion in the preoperative staging of neuroendocrine tumors (NET) of the pancreas and the gastrointestinal tract (GIT). Thirty-eight patients suffering from a metastasized NET with location of the primary in the pancreas (n=15) or the GIT (n=23) were examined by somatostatin receptor scintigraphy (SRS) and computed tomography (CT). Consecutive image registration and fusion were performed using custom-built software integrated in AVS/Express (Advanced Visual Systems, Waltham, MA, USA). Registration was performed by a voxel-based algorithm based on normalized mutual information. Image fusion was feasible in 36/38 patients. A total of 87 foci were assigned to anatomical regions (e.g. gut, pancreas, liver, lymph node or others) by two independent observers in both SRS and SRS/CT fusion images. The assignments used a binary ranking system (1=”definite”, 0=”not definite”). These results were then retrospectively compared to the classification of the foci, based on postoperative histology or clinical follow-up. Imaging by SRS allowed a definite anatomical assignment in 57% (50/87) and 61% (53/87) of all lesions in the case of observers A and B, respectively. Image fusion improved the topographic assignment to 91% (79/87) and to 93% (81/87). The number classified as “definite” by both observers increased from 54% (47/87) to 86% (77/87). The increase in definite assignments was highly significant for both observers (P<0.0001 for each). In the case of foci classified as liver metastases, image fusion allowed improved assignment to the corresponding liver segment from 45% (18/40) to 98% (39/40) and from 58% (23/40) to 100% (40/40) by observers A and B, respectively. Furthermore, the improved assignment of foci classified as lesions by image fusion was relevant for therapy in 7/36 patients (19%). Therefore, the image fusion technique presented herein appears to be a very useful method for clinical routine.