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

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Date

2023-12

Authors

Bomhals, Birte
Cossement, Lara
Maes, Alex
Sathekge, Mike Machaba
Mokoala, Kgomotso M.G.
Sathekge, Chabi
Ghysen, Katrien
Van de Wiele, Christophe

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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.

URI

http://hdl.handle.net/2263/96250

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Research Articles (Nuclear Medicine)
Research Articles (University of Pretoria)
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