ArticleJournal of clinical medicine2023
Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Radiomics-based classification of medication-related osteonecrosis of the jaw using panoramic radiographs.Oral radiology · 2025Article
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance.Bioengineering (Basel, Switzerland) · 2025Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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