Cancer prediction modeling from volumetric data

Marius Paltanea; Sabin Tabirca; Yin Jie Chen; Mark Tangney

doi:10.1109/SYNASC.2009.36

Cancer prediction modeling from volumetric data

Marius Paltanea
, Sabin Tabirca
, Yin Jie Chen
, Mark Tangney

School of Computer Science and Information Technology

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

Abstract

This paper introduces a method for cancer prediction based on the Fister-Panetta (FP) model for cancer growth. The FP equation includes a component for the tumor growth which describes its natural evolution without any treatment. The second component of the FP equation is represented by the contribution of the treatment scheme. Our prediction uses these two components to predict the evolution of the tumor in the near future. The prediction model uses the real information about the tumor growth in this two cases to find the best mathematical approximation with the FP equation. Then this equation is used to predict the evolution in the near future of the tumor.

Original language	English
Title of host publication	SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Pages	162-167
Number of pages	6
DOIs	https://doi.org/10.1109/SYNASC.2009.36
Publication status	Published - 2009
Event	11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2009 - Timisoara, Romania Duration: 26 Sep 2009 → 29 Sep 2009

Publication series

Name	SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

Conference

Conference	11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2009
Country/Territory	Romania
City	Timisoara
Period	26/09/09 → 29/09/09

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Fister-panetta model
Prediction
Tumor growth

Access to Document

10.1109/SYNASC.2009.36

Cite this

Paltanea, M., Tabirca, S., Chen, Y. J., & Tangney, M. (2009). Cancer prediction modeling from volumetric data. In SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 162-167). Article 5460854 (SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing). https://doi.org/10.1109/SYNASC.2009.36

@inproceedings{1ee7a86f881e4ad6b532892e5a242d8f,

title = "Cancer prediction modeling from volumetric data",

abstract = "This paper introduces a method for cancer prediction based on the Fister-Panetta (FP) model for cancer growth. The FP equation includes a component for the tumor growth which describes its natural evolution without any treatment. The second component of the FP equation is represented by the contribution of the treatment scheme. Our prediction uses these two components to predict the evolution of the tumor in the near future. The prediction model uses the real information about the tumor growth in this two cases to find the best mathematical approximation with the FP equation. Then this equation is used to predict the evolution in the near future of the tumor.",

keywords = "Fister-panetta model, Prediction, Tumor growth",

author = "Marius Paltanea and Sabin Tabirca and Chen, \{Yin Jie\} and Mark Tangney",

year = "2009",

doi = "10.1109/SYNASC.2009.36",

language = "English",

isbn = "9780769539645",

series = "SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing",

pages = "162--167",

booktitle = "SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing",

note = "11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2009 ; Conference date: 26-09-2009 Through 29-09-2009",

}

Paltanea, M, Tabirca, S, Chen, YJ & Tangney, M 2009, Cancer prediction modeling from volumetric data. in SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing., 5460854, SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pp. 162-167, 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2009, Timisoara, Romania, 26/09/09. https://doi.org/10.1109/SYNASC.2009.36

Cancer prediction modeling from volumetric data. / Paltanea, Marius; Tabirca, Sabin; Chen, Yin Jie et al.
SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. 2009. p. 162-167 5460854 (SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing).

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

TY - GEN

T1 - Cancer prediction modeling from volumetric data

AU - Paltanea, Marius

AU - Tabirca, Sabin

AU - Chen, Yin Jie

AU - Tangney, Mark

PY - 2009

Y1 - 2009

N2 - This paper introduces a method for cancer prediction based on the Fister-Panetta (FP) model for cancer growth. The FP equation includes a component for the tumor growth which describes its natural evolution without any treatment. The second component of the FP equation is represented by the contribution of the treatment scheme. Our prediction uses these two components to predict the evolution of the tumor in the near future. The prediction model uses the real information about the tumor growth in this two cases to find the best mathematical approximation with the FP equation. Then this equation is used to predict the evolution in the near future of the tumor.

AB - This paper introduces a method for cancer prediction based on the Fister-Panetta (FP) model for cancer growth. The FP equation includes a component for the tumor growth which describes its natural evolution without any treatment. The second component of the FP equation is represented by the contribution of the treatment scheme. Our prediction uses these two components to predict the evolution of the tumor in the near future. The prediction model uses the real information about the tumor growth in this two cases to find the best mathematical approximation with the FP equation. Then this equation is used to predict the evolution in the near future of the tumor.

KW - Fister-panetta model

KW - Prediction

KW - Tumor growth

UR - https://www.scopus.com/pages/publications/77953076363

U2 - 10.1109/SYNASC.2009.36

DO - 10.1109/SYNASC.2009.36

M3 - Conference proceeding

AN - SCOPUS:77953076363

SN - 9780769539645

T3 - SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

SP - 162

EP - 167

BT - SYNASC 2009 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

T2 - 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2009

Y2 - 26 September 2009 through 29 September 2009

ER -

Cancer prediction modeling from volumetric data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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