Abstract
Radiomic features, numeric values extracted from a region of interest (ROI) in medical images, can be used to train prognostic models for various types of cancer. However, in locally advanced diseases, more than one lesion may be present. Using the information contained in multiple regions increases the complexity and necessitates additional processing. Here, we tested seven strategies of handling multiple regions in radiomic-based regularized Cox regression for predicting metastasis-free survival using a cohort of 115 non-small cell lung cancer patients. We have found that using all ROIs to fit the model allowed for better results than using only the largest ROI, achieving c-indexes of 0.617 and 0.581, respectively.
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Acknowledgements
This work was supported by the 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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A. M. W. designed the computational study and methodology, performed the analysis and visualizations, and wrote the initial draft of the manuscript. D. B. extracted the radiomic features and edited the manuscript. E. K. participated in the study design, conceptualization and methodology, prepared the data for analysis and edited the manuscript. A. D’A. and I. G. assembled and prepared the imaging data. I. D. S. and R. S. provided and assembled the patient database. S.G. participated in the analysis and manuscript writing. K. F. provided administrative support. A. S. participated in the study design and conceptualization and supervised the project. All authors have read and approved the final version of the manuscript.
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Wilk, A.M. et al. (2024). Improving the Predictive Ability of Radiomics-Based Regression Survival Models Through Incorporating Multiple Regions of Interest. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_13
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DOI: https://doi.org/10.1007/978-3-031-38430-1_13
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