Principal component analysis applied to radiomics data : added value for separating benign from malignant solitary pulmonary nodules
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Date
Authors
Bomhals, Birte
Cossement, Lara
Maes, Alex
Sathekge, Mike Machaba
Mokoala, Kgomotso M.G.
Sathekge, Chabi
Ghysen, Katrien
Van de Wiele, Christophe
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Please read abstract in article.
Description
DATA AVAILABILITY STATEMENT: Data may be obtained via the last author following a reasonable request and following approval from our Ethics Committee.
Keywords
Solitary pulmonary nodules, Texture features, Benign vs. malignant, Logistic binomial regression, Principal component analysis (PCA), SDG-03: Good health and well-being
Sustainable Development Goals
SDG-03:Good heatlh and well-being
Citation
Bomhals, B.; Cossement, L.; Maes, A.; Sathekge, M.; Mokoala, K.M.G.; Sathekge, C.; Ghysen, K.; Van deWiele, C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. Journal of Clinical Medicine. 2023, 12, 7731. https://doi.org/10.3390/jcm12247731.