Automated Decision Support for Bone Scintigraphy

Summary, in English

A quantitative analysis of metastatic bone involvement can be an important prognostic indicator of survival or a tool in monitoring treatment response in patients with cancer The purpose of this study was to develop a completely automated decision support system for whole-body bone scans using image analysis and artificial neural networks. The study population consisted of 795 whole-body bone scans. The decision support system first detects and classifies individual hotspots as being metastatic or not. A second prediction model then classifies the scan regarding metastatic disease on a patient level. The test set sensitivity and specificity was 95% and 64% respectively, corresponding to 95% area under the receiver operating characteristics curve.

• Author: Mattias Ohlsson; Karl Sjostrand; Jens Richter; Reza Kaboteh; May Sadik; Lars Edenbrandt
• Department/s: Computational Biology and Biological Physics - Has been reorganised; Nuclear medicine, Malmö
• Publishing year: 2009
• Language: English
• Pages: 298-303
• Publication/Series: 2009 22nd IEEE International Symposium on Computer-Based Medical Systems
• Links: http://dx.doi.org/10.1109/CBMS.2009.5255270
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Radiology, Nuclear Medicine and Medical Imaging
• Conference name: 22nd IEEE International Symposium on Computer-Based Medical Systems
• Conference date: 2009-08-03 - 2009-08-04
• Status: Published
• Research group: Nuclear medicine, Malmö
• ISBN/ISSN/Other: ISSN: 1063-7125; ISBN: 978-1-4244-4879-1