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

Research output: Contribution to journalArticle

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.

Details

Authors
  • May Sadik
  • Iman Hamadeh
  • Pierre Nordblom
  • Madis Suurkula
  • Peter Höglund
  • Mattias Ohlsson
  • Lars Edenbrandt
Organisations
External organisations
  • Sahlgrenska University Hospital
  • EXINI Diagnostics AB
  • Skåne University Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • neural networks, radionuclide imaging, computer-assisted diagnosis, bone metastases, image processing
Original languageEnglish
Pages (from-to)1958-1965
JournalJournal of Nuclear Medicine
Volume49
Issue number12
Publication statusPublished - 2008
Publication categoryResearch
Peer-reviewedYes