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...

Ful tanımlama

Kaydedildi:

Detaylı Bibliyografya
Asıl Yazarlar:	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
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Nature Portfolio 2024-04-01
Seri Bilgileri:	Scientific Reports
Online Erişim:	https://doi.org/10.1038/s41598-024-58551-4
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

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

Benzer Materyaller