Abstract
Lung cancer is the most deadly malignancy, with distant metastasis being a major negative prognostic factor. Recently, interest is growing in imaging data as a source of predictors due to the low invasiveness of their acquisition. Using a cohort of 131 patients and a total of 356 ROIs we built a Cox regression model which predicts metastasis and time to its occurrence based on radiomic features extracted from PET/CT images. We employed several variable selection methods, including filtering based on correlation, univariate analysis, recursive elimination and LASSO, and obtained a C-index of 0.7 for the best model. This result shows that radiomic features have great potential as predictors of metastatic relapse, knowledge of which could aid clinicians in planning treatment.
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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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Wilk, A., Borys, D., Fujarewicz, K., d’Amico, A., Suwiński, R., Świerniak, A. (2022). Potential of Radiomics Features for Predicting Time to Metastasis in NSCLC. 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_6
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