Computational modeling in quantitative cancer imaging

Thomas Yankeelov, Nkiruka C. Atuegwu, John C. Gore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years there have been dramatic increases in the range and quality of information available from non-invasive imaging methods so that a number of potentially valuable metrics are now available to quantitatively assess tumor status. Several of these have been used in both pre-clinical studies of animal models and clinical trials involving patients. However, the optimal methods by which these emerging imaging metrics are integrated and applied have yet to be developed. Here we provide an example of the kind of data available from quantitative imaging of cancer, and then propose an approach for how these data can be combined in order to offer a more comprehensive description of tumor growth and treatment response.

Original languageEnglish (US)
Title of host publication2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009
DOIs
StatePublished - Oct 20 2009
Event2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009 - Oak Ridge, TN, United States
Duration: Mar 18 2009Mar 19 2009

Publication series

Name2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009

Other

Other2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009
CountryUnited States
CityOak Ridge, TN
Period3/18/093/19/09

Fingerprint

Imaging techniques
Tumors
Neoplasms
Animals
Animal Models
Clinical Trials
Growth
Therapeutics
Clinical Studies

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Yankeelov, T., Atuegwu, N. C., & Gore, J. C. (2009). Computational modeling in quantitative cancer imaging. In 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009 [5090456] (2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009). https://doi.org/10.1109/BSEC.2009.5090456

Computational modeling in quantitative cancer imaging. / Yankeelov, Thomas; Atuegwu, Nkiruka C.; Gore, John C.

2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009. 2009. 5090456 (2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yankeelov, T, Atuegwu, NC & Gore, JC 2009, Computational modeling in quantitative cancer imaging. in 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009., 5090456, 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009, 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009, Oak Ridge, TN, United States, 3/18/09. https://doi.org/10.1109/BSEC.2009.5090456
Yankeelov T, Atuegwu NC, Gore JC. Computational modeling in quantitative cancer imaging. In 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009. 2009. 5090456. (2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009). https://doi.org/10.1109/BSEC.2009.5090456
Yankeelov, Thomas ; Atuegwu, Nkiruka C. ; Gore, John C. / Computational modeling in quantitative cancer imaging. 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009. 2009. (2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009).
@inproceedings{7afecd2a62a4465b844e7f603364d814,
title = "Computational modeling in quantitative cancer imaging",
abstract = "In recent years there have been dramatic increases in the range and quality of information available from non-invasive imaging methods so that a number of potentially valuable metrics are now available to quantitatively assess tumor status. Several of these have been used in both pre-clinical studies of animal models and clinical trials involving patients. However, the optimal methods by which these emerging imaging metrics are integrated and applied have yet to be developed. Here we provide an example of the kind of data available from quantitative imaging of cancer, and then propose an approach for how these data can be combined in order to offer a more comprehensive description of tumor growth and treatment response.",
author = "Thomas Yankeelov and Atuegwu, {Nkiruka C.} and Gore, {John C.}",
year = "2009",
month = "10",
day = "20",
doi = "10.1109/BSEC.2009.5090456",
language = "English (US)",
isbn = "9781424438372",
series = "2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009",
booktitle = "2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009",

}

TY - GEN

T1 - Computational modeling in quantitative cancer imaging

AU - Yankeelov, Thomas

AU - Atuegwu, Nkiruka C.

AU - Gore, John C.

PY - 2009/10/20

Y1 - 2009/10/20

N2 - In recent years there have been dramatic increases in the range and quality of information available from non-invasive imaging methods so that a number of potentially valuable metrics are now available to quantitatively assess tumor status. Several of these have been used in both pre-clinical studies of animal models and clinical trials involving patients. However, the optimal methods by which these emerging imaging metrics are integrated and applied have yet to be developed. Here we provide an example of the kind of data available from quantitative imaging of cancer, and then propose an approach for how these data can be combined in order to offer a more comprehensive description of tumor growth and treatment response.

AB - In recent years there have been dramatic increases in the range and quality of information available from non-invasive imaging methods so that a number of potentially valuable metrics are now available to quantitatively assess tumor status. Several of these have been used in both pre-clinical studies of animal models and clinical trials involving patients. However, the optimal methods by which these emerging imaging metrics are integrated and applied have yet to be developed. Here we provide an example of the kind of data available from quantitative imaging of cancer, and then propose an approach for how these data can be combined in order to offer a more comprehensive description of tumor growth and treatment response.

UR - http://www.scopus.com/inward/record.url?scp=70349999205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349999205&partnerID=8YFLogxK

U2 - 10.1109/BSEC.2009.5090456

DO - 10.1109/BSEC.2009.5090456

M3 - Conference contribution

AN - SCOPUS:70349999205

SN - 9781424438372

T3 - 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009

BT - 2009 1st Annual ORNL Biomedical Science and Engineering Conference, BSEC 2009

ER -