Loading…
A novel statistical analysis method to improve the detection of hepatic foci of 111In-octreotide in SPECT/CT imaging
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 c...
Saved in:
Published in: | EJNMMI physics 2016-01, Vol.3 (1), Article 1 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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
|
---|---|
ISSN: | 2197-7364 2197-7364 |
DOI: | 10.1186/s40658-016-0137-4 |