@inproceedings{3f1f189ab2ea4a28ae0983b2437dc257,
title = "Automated Decision Support for Bone Scintigraphy",
abstract = "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 and Karl Sjostrand and Jens Richter and Reza Kaboteh and May Sadik and Lars Edenbrandt",
year = "2009",
doi = "10.1109/CBMS.2009.5255270",
language = "English",
isbn = "978-1-4244-4879-1",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "298--303",
booktitle = "2009 22nd IEEE International Symposium on Computer-Based Medical Systems",
address = "United States",
note = "22nd IEEE International Symposium on Computer-Based Medical Systems ; Conference date: 03-08-2009 Through 04-08-2009",
}
