Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

Abstract The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography im...

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Bibliographic Details
Main Authors: Thomas Louis, François Lucia, François Cousin, Carole Mievis, Nicolas Jansen, Bernard Duysinx, Romain Le Pennec, Dimitris Visvikis, Malik Nebbache, Martin Rehn, Mohamed Hamya, Margaux Geier, Pierre-Yves Salaun, Ulrike Schick, Mathieu Hatt, Philippe Coucke, Pierre Lovinfosse, Roland Hustinx
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-58551-4

Internet

https://doi.org/10.1038/s41598-024-58551-4

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