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Updated: Jul 16, 2026
Harnessing the Bioorthogonal Inverse Electron Demand Diels-Alder Cycloaddition for Pretargeted PET Imaging
Published on: February 3, 2015
A novel statistical analysis method to improve the detection of hepatic foci of (111)In-octreotide in SPECT/CT
Tobias Magnander1,2, E Wikberg3, J Svensson4
1Department of Radiation Physics, Institute of Clinical Sciences at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. tobias.magnander@vgregion.se.
Background:
Low uptake ratios, high noise, poor resolution, and low contrast all combine to make the detection of neuroendocrine liver tumours by (111)In-octreotide single photon emission tomography (SPECT) imaging a challenge. The aim of this study was to develop a segmentation analysis method that could improve the accuracy of hepatic neuroendocrine tumour detection.
Methods:
Our novel segmentation was benchmarked by a retrospective analysis of patients categorized as either (111)In-octreotide positive ((111)In-octreotide(+)) or (111)In-octreotide negative ((111)In-octreotide(-)) for liver tumours. Following a 3-year follow-up period, involving multiple imaging modalities, we further segregated (111)In-octreotide-negative patients into two groups: one with no confirmed liver tumours ((111)In-octreotide(-)/radtech(-)) and the other, now diagnosed with liver tumours ((111)In-octreotide(-)/radtech(+)). We retrospectively applied our segmentation analysis to see if it could have detected these previously missed tumours using (111)In-octreotide. Our methodology subdivided the liver and determined normalized numbers of uptake foci (nNUF), at various threshold values, using a connected-component labelling algorithm. Plots of nNUF against the threshold index (ThI) were generated. ThI was defined as follows: ThI = (c max - c thr)/c max, where c max is the maximal threshold value for obtaining at least one, two voxel sized, uptake focus; c thr is the voxel threshold value. The maximal divergence between the nNUF values for (111)In-octreotide(-)/radtech(-), and (111)In-octreotide(+) livers, was used as the optimal nNUF value for tumour detection. We also corrected for any influence of the mean activity concentration on ThI. The nNUF versus ThI method (nNUFTI) was then used to reanalyze the (111)In-octreotide(-)/radtech(-) and (111)In-octreotide(-)/radtech(+) groups.
Results:
Of a total of 53 (111)In-octreotide(-) patients, 40 were categorized as (111)In-octreotide(-)/radtech(-) and 13 as (111)In-octreotide(-)/radtech(+) group. Optimal separation of the nNUF values for (111)In-octreotide(-)/radtech(-) and (111)In-octreotide(+) groups was defined at the nNUF value of 0.25, to the right of the bell shaped nNUFTI curve. ThIs at this nNUF value were dependent on the mean activity concentration and therefore normalized to generate nThI; a significant difference in nThI values was found between the (111)In-octreotide(-)/radtech(-) and the (111)In-octreotide(-)/radtech(+) groups (P < 0.01). As a result, four of the 13 (111)In-octreotide(-)/radtech(+) livers were redesigned as (111)In-octreotide(+).
Conclusions:
The nNUFTI method has the potential to improve the diagnosis of liver tumours using (111)In-octreotide.
