Conference paper
Automated decision support for bone scintigraphy
Department of Theoretical Physics, Lund University, Lund, Sweden1
Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital Gothenburg, Sweden2
EXINI Diagnostics AB, Lund, Sweden3
Department of Clinical Sciences, Lund University, Malmö, Sweden4
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.
| Language: | English |
|---|---|
| Year: | 2009 |
| Pages: | 1-6 |
| Proceedings: | 2009 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS) |
| ISBN: | 1424448786 , 1424448794 , 1509071865 , 9781424448784 , 9781424448791 and 9781509071869 |
| ISSN: | 10637125 |
| Types: | Conference paper |
| DOI: | 10.1109/CBMS.2009.5255270 |
Artificial neural networks Bones Cancer Decision support systems Image analysis Medical treatment Metastasis Patient monitoring Predictive models Testing artificial neural networks automated decision support system bone bone scintigraphy cancer cancer treatment monitoring decision support systems medical computing metastatic bone involvement neural nets orthopaedics radioisotope imaging receiver operating characteristics sensitivity analysis tumours whole-body bone scans
