Radiomics-Based Lung Nodule Classification with Stochastic Search Variable Selection and Bayesian Logistic Regression
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Background
Radiomics is an emerging field in medical science that involves extracting quantitative features from medical images using data characterization algorithms. These features, known as radiomic features, provide a quantitative approach to medical image analysis. In the context of lung cancer, radiomics-based approaches are transforming disease management by improving early detection, diagnosis, prognosis, and treatment decision-making.
Objective
This study aimed to explore the utility of Bayesian methods, specifically Stochastic Variable Selection and Shrinkage (SVSS) and Bayesian logistic regression, in the radiomics-based classification of small lung nodules with limited training data.
Methods
The Bayesian approach was compared to frequentist Lasso logistic regression on the test set. The performance of both methods was evaluated to determine their viability in the classification of small lung nodules.
Results
The Bayesian approach matched the performance of frequentist Lasso logistic regression on the test set, demonstrating its viability as an alternative approach. Annulus GLCM Entrop LLL was consistently identified as a feature positively influencing small lung nodule malignancy prediction across multiple models. This finding enhances confidence in the effect of this feature, suggesting that future Bayesian analyses can incorporate this information for greater reliability in feature selection and coefficient estimates.
Conclusion
This study highlights the potential of Bayesian methods to address the challenges of limited data in medical image analysis, offering a robust alternative to traditional statistical approaches and contributing to improved clinical decision-making.
