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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.

A novel statistical analysis method to improve the detection of hepatic foci of 111In-octreotide in SPECT/CT imaging