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A study on the value of computer-assisted assessment for SPECT/CT-scans in sentinel lymph node diagnostics of penile cancer as well as clinical reliability and morbidity of this procedure

View Article: PubMed Central - PubMed

ABSTRACT

Background: Because of the increasing importance of computer-assisted post processing of image data in modern medical diagnostic we studied the value of an algorithm for assessment of single photon emission computed tomography/computed tomography (SPECT/CT)-data, which has been used for the first time for lymph node staging in penile cancer with non-palpable inguinal lymph nodes. In the guidelines of the relevant international expert societies, sentinel lymph node-biopsy (SLNB) is recommended as a diagnostic method of choice. The aim of this study is to evaluate the value of the afore-mentioned algorithm and in the clinical context the reliability and the associated morbidity of this procedure.

Methods: Between 2008 and 2015, 25 patients with invasive penile cancer and inconspicuous inguinal lymph node status underwent SLNB after application of the radiotracer Tc-99m labelled nanocolloid. We recorded in a prospective approach the reliability and the complication rate of the procedure. In addition, we evaluated the results of an algorithm for SPECT/CT-data assessment of these patients.

Results: SLNB was carried out in 44 groins of 25 patients. In three patients, inguinal lymph node metastases were detected via SLNB. In one patient, bilateral lymph node recurrence of the groins occurred after negative SLNB. There was a false-negative rate of 4 % in relation to the number of patients (1/25), resp. 4.5 % in relation to the number of groins (2/44). Morbidity was 4 % in relation to the number of patients (1/25), resp. 2.3 % in relation to the number of groins (1/44). The results of computer-assisted assessment of SPECT/CT data for sentinel lymph node (SLN)-diagnostics demonstrated high sensitivity of 88.8 % and specificity of 86.7 %.

Conclusions: SLNB is a very reliable method, associated with low morbidity. Computer-assisted assessment of SPECT/CT data of the SLN-diagnostics shows high sensitivity and specificity. While it cannot replace the assessment by medical experts, it can still provide substantial supplement and assistance.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of hot spot detection and segmentation by SPECT in a 2D example. a Original image with two hot spots with their respective local maxima (“A” and “B”). b Result of region growing from local maxima “A” (borders with dashed contour). Note that the bottom of the smaller region is also absorbed as it met the region growing criteria. c Result of the region growing from local maxima “B” (borders with dashed contour). Note that comon voxels were excluded from both regions. d Segmented regions (black contour)
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Fig3: Schematic representation of hot spot detection and segmentation by SPECT in a 2D example. a Original image with two hot spots with their respective local maxima (“A” and “B”). b Result of region growing from local maxima “A” (borders with dashed contour). Note that the bottom of the smaller region is also absorbed as it met the region growing criteria. c Result of the region growing from local maxima “B” (borders with dashed contour). Note that comon voxels were excluded from both regions. d Segmented regions (black contour)

Mentions: For definition for the findings (hot spots) in the SPECT a moving 3 × 3 × 3 voxel mask was applied in order to detect local maxima positions serving as seed point for segmentation. A recursive region-growing algorithm was used in all detected focus areas (hot spots). Employing this region-growing process, every defined focus area was compared with its 26 neighboring voxels in an iterative process to achieve exact spatial allocation of the findings and thus identification of the focus areas (hot spots). If, in this process, a neighboring voxel was smaller or equal to the actual voxel value, it was allocated to the latter. If, however, there was an increase of the value of the neighboring voxels, it was not included in the give region. If a voxel met the region growing criteria but it was already the member of another already segmented region, it was signed as common voxel. After all hot spots were individually segmented these common regions were deleted from the regions. In this way we were able to ensure that no hot spot was lost or absorbed by the neighboring voxels and that none of them touched one another on the voxel level. Figure 3 shows a graphical representation of the SPECT segmentation steps. As the segmentation approach searched for local maxima in the smallest possible kernel in resampled images, it was ensured that neighboring local maxima voxels with at least one lower voxel value in between could be identified as separate regions. Hence, the only limitation of this approach was the spatial resolution of the SPECT imaging technology. If neighboring hot spots could not be distinguished in the original SPECT image, i. e. if it showed no multiple, but only one local maximum position, the program treated them as a single larger hot spot.Fig. 3


A study on the value of computer-assisted assessment for SPECT/CT-scans in sentinel lymph node diagnostics of penile cancer as well as clinical reliability and morbidity of this procedure
Schematic representation of hot spot detection and segmentation by SPECT in a 2D example. a Original image with two hot spots with their respective local maxima (“A” and “B”). b Result of region growing from local maxima “A” (borders with dashed contour). Note that the bottom of the smaller region is also absorbed as it met the region growing criteria. c Result of the region growing from local maxima “B” (borders with dashed contour). Note that comon voxels were excluded from both regions. d Segmented regions (black contour)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
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getmorefigures.php?uid=PMC5015237&req=5

Fig3: Schematic representation of hot spot detection and segmentation by SPECT in a 2D example. a Original image with two hot spots with their respective local maxima (“A” and “B”). b Result of region growing from local maxima “A” (borders with dashed contour). Note that the bottom of the smaller region is also absorbed as it met the region growing criteria. c Result of the region growing from local maxima “B” (borders with dashed contour). Note that comon voxels were excluded from both regions. d Segmented regions (black contour)
Mentions: For definition for the findings (hot spots) in the SPECT a moving 3 × 3 × 3 voxel mask was applied in order to detect local maxima positions serving as seed point for segmentation. A recursive region-growing algorithm was used in all detected focus areas (hot spots). Employing this region-growing process, every defined focus area was compared with its 26 neighboring voxels in an iterative process to achieve exact spatial allocation of the findings and thus identification of the focus areas (hot spots). If, in this process, a neighboring voxel was smaller or equal to the actual voxel value, it was allocated to the latter. If, however, there was an increase of the value of the neighboring voxels, it was not included in the give region. If a voxel met the region growing criteria but it was already the member of another already segmented region, it was signed as common voxel. After all hot spots were individually segmented these common regions were deleted from the regions. In this way we were able to ensure that no hot spot was lost or absorbed by the neighboring voxels and that none of them touched one another on the voxel level. Figure 3 shows a graphical representation of the SPECT segmentation steps. As the segmentation approach searched for local maxima in the smallest possible kernel in resampled images, it was ensured that neighboring local maxima voxels with at least one lower voxel value in between could be identified as separate regions. Hence, the only limitation of this approach was the spatial resolution of the SPECT imaging technology. If neighboring hot spots could not be distinguished in the original SPECT image, i. e. if it showed no multiple, but only one local maximum position, the program treated them as a single larger hot spot.Fig. 3

View Article: PubMed Central - PubMed

ABSTRACT

Background: Because of the increasing importance of computer-assisted post processing of image data in modern medical diagnostic we studied the value of an algorithm for assessment of single photon emission computed tomography/computed tomography (SPECT/CT)-data, which has been used for the first time for lymph node staging in penile cancer with non-palpable inguinal lymph nodes. In the guidelines of the relevant international expert societies, sentinel lymph node-biopsy (SLNB) is recommended as a diagnostic method of choice. The aim of this study is to evaluate the value of the afore-mentioned algorithm and in the clinical context the reliability and the associated morbidity of this procedure.

Methods: Between 2008 and 2015, 25 patients with invasive penile cancer and inconspicuous inguinal lymph node status underwent SLNB after application of the radiotracer Tc-99m labelled nanocolloid. We recorded in a prospective approach the reliability and the complication rate of the procedure. In addition, we evaluated the results of an algorithm for SPECT/CT-data assessment of these patients.

Results: SLNB was carried out in 44 groins of 25 patients. In three patients, inguinal lymph node metastases were detected via SLNB. In one patient, bilateral lymph node recurrence of the groins occurred after negative SLNB. There was a false-negative rate of 4 % in relation to the number of patients (1/25), resp. 4.5 % in relation to the number of groins (2/44). Morbidity was 4 % in relation to the number of patients (1/25), resp. 2.3 % in relation to the number of groins (1/44). The results of computer-assisted assessment of SPECT/CT data for sentinel lymph node (SLN)-diagnostics demonstrated high sensitivity of 88.8 % and specificity of 86.7 %.

Conclusions: SLNB is a very reliable method, associated with low morbidity. Computer-assisted assessment of SPECT/CT data of the SLN-diagnostics shows high sensitivity and specificity. While it cannot replace the assessment by medical experts, it can still provide substantial supplement and assistance.

No MeSH data available.


Related in: MedlinePlus