Automated decision support for bone scintigraphy
- Resource Type
- Conference
- Authors
- Ohlsson, Mattias; Kaboteh, Reza; Sadik, May; Suurkula, Madis; Lomsky, Milan; Gjertsson, Peter; Sjostrand, Karl; Richter, Jens; Edenbrandt, Lars
- Source
- 2009 22nd IEEE International Symposium on Computer-Based Medical Systems Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on. :1-6 Aug, 2009
- Subject
- Bioengineering
Computing and Processing
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Bones
Metastasis
Decision support systems
Patient monitoring
Medical treatment
Cancer
Image analysis
Artificial neural networks
Predictive models
Testing
- Language
- ISSN
- 1063-7125
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.