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Computer-Assisted Interpretation of Planar Whole-Body Bone Scans.

Sadik, May; Hamadeh, Iman; Nordblom, Pierre; Suurkula, Madis; Höglund, Peter LU ; Ohlsson, Mattias LU and Edenbrandt, Lars LU (2008) In Journal of Nuclear Medicine 49(12). p.1958-1965
Abstract
The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. METHODS: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior views, were obtained after an injection of (99m)Tc-methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton and automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the... (More)
The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. METHODS: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior views, were obtained after an injection of (99m)Tc-methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton and automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, 1 classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group, we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data, including the results of laboratory tests, and available diagnostic images, such as from MRI, CT, and radiography, from a mean follow-up period of 4.8 y. RESULTS: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. False-positive classification of metastases was made in 4 of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%. CONCLUSION: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
neural networks, radionuclide imaging, computer-assisted diagnosis, bone metastases, image processing
in
Journal of Nuclear Medicine
volume
49
issue
12
pages
1958 - 1965
publisher
Society of Nuclear Medicine
external identifiers
  • wos:000261535800016
  • pmid:18997038
  • scopus:57149087071
ISSN
0161-5505
DOI
10.2967/jnumed.108.055061
language
English
LU publication?
yes
id
23aa5336-4ac4-47ae-851b-b9170767f1a5 (old id 1271644)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/18997038?dopt=Abstract
date added to LUP
2008-12-03 13:01:27
date last changed
2017-09-17 05:50:05
@article{23aa5336-4ac4-47ae-851b-b9170767f1a5,
  abstract     = {The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. METHODS: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior views, were obtained after an injection of (99m)Tc-methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton and automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, 1 classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group, we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data, including the results of laboratory tests, and available diagnostic images, such as from MRI, CT, and radiography, from a mean follow-up period of 4.8 y. RESULTS: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. False-positive classification of metastases was made in 4 of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%. CONCLUSION: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential.},
  author       = {Sadik, May and Hamadeh, Iman and Nordblom, Pierre and Suurkula, Madis and Höglund, Peter and Ohlsson, Mattias and Edenbrandt, Lars},
  issn         = {0161-5505},
  keyword      = {neural networks,radionuclide imaging,computer-assisted diagnosis,bone metastases,image processing},
  language     = {eng},
  number       = {12},
  pages        = {1958--1965},
  publisher    = {Society of Nuclear Medicine},
  series       = {Journal of Nuclear Medicine},
  title        = {Computer-Assisted Interpretation of Planar Whole-Body Bone Scans.},
  url          = {http://dx.doi.org/10.2967/jnumed.108.055061},
  volume       = {49},
  year         = {2008},
}