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Prediction of lung cancer patients’ response to combined chemo-radiotherapy using a personalized hybrid model

100%

Wołkowicz S., Fujarewicz K., Kurpas M., Łakomiec K., Śmieja J., Suwiński R., Świerniak A.

Mathematica Applicanda

2019

tom 47

nr 2

The paper presents a novel approach to prediction of the combined therapy outcome for non-small lung cancer patients. A hybrid model is proposed, consisting of two parts. The first one is a mathematical model of tumor response to therapy, whose parameters are estimated by a neural network based regressor, constituting the second component of the hybrid model. Estimation is based on data from mass spectrometry of patient blood plasma samples. Comparison of clinical and simulation-based survival curves is used to evaluate the quality of the model.

The paper presents a novel approach to the prediction of the combined therapy outcome for non-small lung cancer patients. A hybrid model is proposed, consisting of two parts. The first one is a mathematical model of tumor response to therapy, whose parameters are expressed as linear function of data from massspectrometry of patient blood plasma samples. These linear functions constitute thesecond component of the hybrid model. A comparison of clinical and simulation-based survival curves is used to evaluate the quality of the model.

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

75%

Kimmel M., Gorlova O. Y.

International Journal of Applied Mathematics and Computer Science

2003

tom 13

nr 3

279-287

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.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Mathematica Applicanda

1 Fujarewicz K.

1 Gorlova O. Y.

1 Kimmel M.

1 Kurpas M.

1 Suwiński R.

1 Wołkowicz S.

1 Łakomiec K.

1 Śmieja J.

1 Świerniak A.

1 2019

1 2003

Wyniki wyszukiwania

Prediction of lung cancer patients’ response to combined chemo-radiotherapy using a personalized hybrid model

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