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
Published in:	Journal of clinical medicine Vol. 12; no. 24; p. 7731
Main Authors:	Bomhals, Birte, Cossement, Lara, Maes, Alex, Sathekge, Mike, Mokoala, Kgomotso M G, Sathekge, Chabi, Ghysen, Katrien, Van de Wiele, Christophe
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-12-2023
Subjects:	Computer-aided medical diagnosis CT imaging Data mining Datasets Diagnosis Lung tumors Medical imaging equipment Methods PET imaging Principal components analysis Radiomics Software Statistical analysis Tomography Variables Belgium texture features principal component analysis solitary pulmonary nodules
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Summary:	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 derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study ( = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2077-0383 2077-0383
DOI:	10.3390/jcm12247731