Automated decision support for bone scintigraphy

M. Ohlsson; R. Kaboteh; M. Sadik; M. Suurkula; M. Lomsky; P. Gjertsson; Karl Sjöstrand; J. Richter; L. Edenbrandt

doi:10.1109/CBMS.2009.5255270

Automated decision support for bone scintigraphy

M. Ohlsson, R. Kaboteh, M. Sadik, M. Suurkula, M. Lomsky, P. Gjertsson, Karl Sjöstrand, J. Richter, L. Edenbrandt

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Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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. ©2009 IEEE.

Original language	English
Title of host publication	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Number of pages	5255270
Publisher	IEEE
Publication date	2009
ISBN (Print)	9781424448784
DOIs	https://doi.org/10.1109/CBMS.2009.5255270
Publication status	Published - 2009
Externally published	Yes
Event	2009 22nd IEEE International Symposium on Computer-Based Medical Systems - Albuquerque, United States Duration: 2 Aug 2009 → 5 Aug 2009 Conference number: 22 https://ieeexplore.ieee.org/xpl/conhome/5230478/proceeding

Conference

Conference	2009 22nd IEEE International Symposium on Computer-Based Medical Systems
Number	22
Country/Territory	United States
City	Albuquerque
Period	02/08/2009 → 05/08/2009
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5230478/proceeding

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CBMS.2009.5255270

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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. {\textcopyright}2009 IEEE.",

author = "M. Ohlsson and R. Kaboteh and M. Sadik and M. Suurkula and M. Lomsky and P. Gjertsson and Karl Sj{\"o}strand and J. Richter and L. Edenbrandt",

year = "2009",

doi = "10.1109/CBMS.2009.5255270",

language = "English",

isbn = "9781424448784",

booktitle = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

publisher = "IEEE",

address = "United States",

note = "2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009 ; Conference date: 02-08-2009 Through 05-08-2009",

url = "https://ieeexplore.ieee.org/xpl/conhome/5230478/proceeding",

}

Ohlsson, M, Kaboteh, R, Sadik, M, Suurkula, M, Lomsky, M, Gjertsson, P, Sjöstrand, K, Richter, J & Edenbrandt, L 2009, Automated decision support for bone scintigraphy. in Proceedings - IEEE Symposium on Computer-Based Medical Systems. IEEE, 2009 22nd IEEE International Symposium on Computer-Based Medical Systems, Albuquerque, New Mexico, United States, 02/08/2009. https://doi.org/10.1109/CBMS.2009.5255270

TY - GEN

T1 - Automated decision support for bone scintigraphy

AU - Ohlsson, M.

AU - Kaboteh, R.

AU - Sadik, M.

AU - Suurkula, M.

AU - Lomsky, M.

AU - Gjertsson, P.

AU - Sjöstrand, Karl

AU - Richter, J.

AU - Edenbrandt, L.

N1 - Conference code: 22

PY - 2009

Y1 - 2009

N2 - 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. ©2009 IEEE.

AB - 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. ©2009 IEEE.

U2 - 10.1109/CBMS.2009.5255270

DO - 10.1109/CBMS.2009.5255270

M3 - Article in proceedings

SN - 9781424448784

BT - Proceedings - IEEE Symposium on Computer-Based Medical Systems

PB - IEEE

T2 - 2009 22nd IEEE International Symposium on Computer-Based Medical Systems

Y2 - 2 August 2009 through 5 August 2009

ER -

Automated decision support for bone scintigraphy

Abstract

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UN SDGs

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