Stochastic models of progression of cancer andtheir use in controlling cancer-related mortality

Marek Kimmel; Olga Gorlova

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 3, page 279-287
  • ISSN: 1641-876X

Abstract

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A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under different scenarios. Genetic indicators of susceptibility to lung cancer should be used to define the highest-risk subgroups of the high-risk behavior population (smokers). The calibration and validation of the model requires applying our techniques to a variety of data sets available, including public registry data of the SEER type, data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study.

How to cite

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Kimmel, Marek, and Gorlova, Olga. "Stochastic models of progression of cancer andtheir use in controlling cancer-related mortality." International Journal of Applied Mathematics and Computer Science 13.3 (2003): 279-287. <http://eudml.org/doc/207642>.

@article{Kimmel2003,
abstract = {A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under different scenarios. Genetic indicators of susceptibility to lung cancer should be used to define the highest-risk subgroups of the high-risk behavior population (smokers). The calibration and validation of the model requires applying our techniques to a variety of data sets available, including public registry data of the SEER type, data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study.},
author = {Kimmel, Marek, Gorlova, Olga},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {simulation; lung cancer; genetic susceptibility; statistical modeling; tumor growth; environmental exposure; environmental eposure; simulations},
language = {eng},
number = {3},
pages = {279-287},
title = {Stochastic models of progression of cancer andtheir use in controlling cancer-related mortality},
url = {http://eudml.org/doc/207642},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Kimmel, Marek
AU - Gorlova, Olga
TI - Stochastic models of progression of cancer andtheir use in controlling cancer-related mortality
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 3
SP - 279
EP - 287
AB - A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under different scenarios. Genetic indicators of susceptibility to lung cancer should be used to define the highest-risk subgroups of the high-risk behavior population (smokers). The calibration and validation of the model requires applying our techniques to a variety of data sets available, including public registry data of the SEER type, data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study.
LA - eng
KW - simulation; lung cancer; genetic susceptibility; statistical modeling; tumor growth; environmental exposure; environmental eposure; simulations
UR - http://eudml.org/doc/207642
ER -

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