Outcome prediction in tumour therapy based on Dempster-Shafer theory

Chunfeng Lian; Su Ruan; Thierry Denoux; Pierre Vera

doi:10.1109/ISBI.2015.7163817

Outcome prediction in tumour therapy based on Dempster-Shafer theory

Chunfeng Lian
, Su Ruan
, Thierry Denoux
, Pierre Vera

Sorbonne Université
Normandie Université
Centre Georges-François Leclerc

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

7 Scopus citations

Abstract

Outcome prediction plays a vital role in cancer treatment. It can help to update and optimize the treatment planning. In this paper, we aim to find discriminant features from both PET images and clinical characteristics, so as to predict the outcome of a treatment to adapt the therapy. As both information sources are imprecise, we propose a novel feature selection method based on Dempster-Shafer theory to tackle this problem. Then, a specific objective function with spar-sity constraint is developed to search for a feature subset that leads to increasing prediction performance and decreasing data imprecision simultaneously. Our approach was applied to two real data sets concerning to lung tumour et esophageal tumour, showing good performance.

Original language	English
Title of host publication	2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Publisher	IEEE Computer Society
Pages	63-66
Number of pages	4
ISBN (Electronic)	9781479923748
DOIs	https://doi.org/10.1109/ISBI.2015.7163817
State	Published - 21 Jul 2015
Externally published	Yes
Event	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 16 Apr 2015 → 19 Apr 2015

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2015-July
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/Territory	United States
City	Brooklyn
Period	16/04/15 → 19/04/15

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Dempster-Shafer Theory
Feature Selection
Outcome Prediction
PET imaging

Access to Document

10.1109/ISBI.2015.7163817

Cite this

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title = "Outcome prediction in tumour therapy based on Dempster-Shafer theory",

abstract = "Outcome prediction plays a vital role in cancer treatment. It can help to update and optimize the treatment planning. In this paper, we aim to find discriminant features from both PET images and clinical characteristics, so as to predict the outcome of a treatment to adapt the therapy. As both information sources are imprecise, we propose a novel feature selection method based on Dempster-Shafer theory to tackle this problem. Then, a specific objective function with spar-sity constraint is developed to search for a feature subset that leads to increasing prediction performance and decreasing data imprecision simultaneously. Our approach was applied to two real data sets concerning to lung tumour et esophageal tumour, showing good performance.",

keywords = "Dempster-Shafer Theory, Feature Selection, Outcome Prediction, PET imaging",

author = "Chunfeng Lian and Su Ruan and Thierry Denoux and Pierre Vera",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",

year = "2015",

month = jul,

day = "21",

doi = "10.1109/ISBI.2015.7163817",

language = "英语",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "63--66",

booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",

}

Lian, C, Ruan, S, Denoux, T & Vera, P 2015, Outcome prediction in tumour therapy based on Dempster-Shafer theory. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015., 7163817, Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, IEEE Computer Society, pp. 63-66, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16/04/15. https://doi.org/10.1109/ISBI.2015.7163817

Outcome prediction in tumour therapy based on Dempster-Shafer theory. / Lian, Chunfeng; Ruan, Su; Denoux, Thierry et al.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. IEEE Computer Society, 2015. p. 63-66 7163817 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July).

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

TY - GEN

T1 - Outcome prediction in tumour therapy based on Dempster-Shafer theory

AU - Lian, Chunfeng

AU - Ruan, Su

AU - Denoux, Thierry

AU - Vera, Pierre

PY - 2015/7/21

Y1 - 2015/7/21

N2 - Outcome prediction plays a vital role in cancer treatment. It can help to update and optimize the treatment planning. In this paper, we aim to find discriminant features from both PET images and clinical characteristics, so as to predict the outcome of a treatment to adapt the therapy. As both information sources are imprecise, we propose a novel feature selection method based on Dempster-Shafer theory to tackle this problem. Then, a specific objective function with spar-sity constraint is developed to search for a feature subset that leads to increasing prediction performance and decreasing data imprecision simultaneously. Our approach was applied to two real data sets concerning to lung tumour et esophageal tumour, showing good performance.

AB - Outcome prediction plays a vital role in cancer treatment. It can help to update and optimize the treatment planning. In this paper, we aim to find discriminant features from both PET images and clinical characteristics, so as to predict the outcome of a treatment to adapt the therapy. As both information sources are imprecise, we propose a novel feature selection method based on Dempster-Shafer theory to tackle this problem. Then, a specific objective function with spar-sity constraint is developed to search for a feature subset that leads to increasing prediction performance and decreasing data imprecision simultaneously. Our approach was applied to two real data sets concerning to lung tumour et esophageal tumour, showing good performance.

KW - Dempster-Shafer Theory

KW - Feature Selection

KW - Outcome Prediction

KW - PET imaging

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DO - 10.1109/ISBI.2015.7163817

M3 - 会议稿件

AN - SCOPUS:84944322737

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 63

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BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015

PB - IEEE Computer Society

T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015

Y2 - 16 April 2015 through 19 April 2015

ER -

Outcome prediction in tumour therapy based on Dempster-Shafer theory

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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