| Title |
A novel statistical analysis method to improve the detection of hepatic foci of 111In-octreotide in SPECT/CT imaging
|
|---|---|
| Published in |
EJNMMI Physics, January 2016
|
| DOI | 10.1186/s40658-016-0137-4 |
| Pubmed ID | |
| Authors |
Tobias Magnander, E. Wikberg, J. Svensson, P. Gjertsson, B. Wängberg, M. Båth, Peter Bernhardt |
| Abstract |
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. 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. 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(+). The nNUFTI method has the potential to improve the diagnosis of liver tumours using (111)In-octreotide. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 20% |
| Austria | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 40% |
| Other | 1 | 20% |
| Student > Master | 1 | 20% |
| Unknown | 1 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 80% |
| Unknown | 1 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 March 2016.
All research outputs
#13,050,328
of 23,577,654 outputs
Outputs from EJNMMI Physics
#43
of 190 outputs
Outputs of similar age
#177,735
of 397,857 outputs
Outputs of similar age from EJNMMI Physics
#3
of 4 outputs
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