Abstract
Lung cancer is the most common cause of cancer-related death worldwide. One of the most significant negative prognostic factors is the occurrence of metastasis. Recently, one of the promising way to diagnose cancer samples is to use the image data (PET, CT etc.) and calculated on the basis of these images so called radiomic features. In this paper we present the attempt to use the radiomic features to predict the metastasis for lung cancer patients. We applied and compared three feature selection methods and two classification methods: logistic regression and support vector machines. The obtained accuracy of the best classifier confirms the potential of the radiomic data in prediction of metastasis in lung cancer.
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Acknowledgement
This work was supported by Polish National Science Centre, grant number: UMO-2020/37/B/ST6/01959 and Silesian University of Technology statutory research funds. Calculations were performed on the Ziemowit computer cluster in the Laboratory of Bioinformatics and Computational Biology created in the EU Innovative Economy Programme POIG.02.01.00-00-166/08 and expanded in the POIG.02.03.01-00-040/13 project.
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Fujarewicz, K., Wilk, A., Borys, D., d’Amico, A., Suwiński, R., Świerniak, A. (2022). Machine Learning Approach to Predict Metastasis in Lung Cancer Based on Radiomic Features. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_4
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DOI: https://doi.org/10.1007/978-3-031-21967-2_4
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