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

Overview of attention for article published in EJNMMI Physics, January 2016
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  • Among the highest-scoring outputs from this source (#43 of 190)
  • Above-average Attention Score compared to outputs of the same age (55th percentile)

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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

Tobias Magnander, E. Wikberg, J. Svensson, P. Gjertsson, B. Wängberg, M. Båth, Peter Bernhardt


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.

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Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 40%
Unspecified 1 20%
Other 1 20%
Student > Master 1 20%
Readers by discipline Count As %
Medicine and Dentistry 4 80%
Unspecified 1 20%
Attention Score in Context

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.
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Outputs of similar age from EJNMMI Physics
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Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 190 research outputs from this source. They receive a mean Attention Score of 2.5. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 397,857 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.