Principal component analysis applied to radiomics data : added value for separating benign from malignant solitary pulmonary nodules

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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.