Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules

Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were der...

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Bibliographic Details
Main Authors: Birte Bomhals, Lara Cossement, Alex Maes, Mike Sathekge, Kgomotso M. G. Mokoala, Chabi Sathekge, Katrien Ghysen, Christophe Van de Wiele
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Journal of Clinical Medicine
Subjects:
solitary pulmonary nodules
texture features
principal component analysis
Online Access:https://www.mdpi.com/2077-0383/12/24/7731

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