Authors:
Andrzej Swierniak
1
;
Emilia Kozłowska
1
;
Krzysztof Fujarewicz
1
;
Damian Borys
1
;
Agata Wilk
1
;
Jaroslaw Smieja
1
and
Rafal Suwinski
2
Affiliations:
1
Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
;
2
The 2nd Radiotherapy and Chemotherapy Clinic, M. Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Wybrzeze Armii Krajowej 15, Gliwice, Poland
Keyword(s):
Medical Image Processing, NSCLC, AI Based Models, Metastases, Survival Analysis.
Abstract:
The aim of this paper is to present goals and preliminary results of our project devoted to system engineering approach in prediction of metastases in lung cancer. More specifically we consider existing and develop new methods of system modeling, machine learning, signal processing and intelligent control to find biomarkers enabling prediction of risk of tumor spread and colonization of distant organs in non-small-cell lung carcinoma basing on clinical data and medical images. The results could bring us knowledge about the dynamics and origin of metastatic dissemination of lung cancer. By dynamics, we understand when and where a tumor will disseminate, and by origin we mean dissemination path (directly from original tumor or through lymphatic nodes). This information is very valuable for clinicians, as it could guide the personalized treatment of lung cancer patients. The results will elucidate important issues concerning prediction of individual progress of cancer and treatment outc
ome in oncology. They will provide both theoretical and simulation tools to support decision making and diagnostics in oncology, on the basis of individual patient state.
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