Design and Implementation of a No-code Graphical User Interface Application for Radiomics Analysis

This study aimed to develop an integrated application that enables the intuitive operation of each radiomics analysis process (image input, feature extraction, dimensionality reduction, model construction, and evaluation) with a graphical user interface (GUI) and no code.

Tkinter, a Python standard library, was used to design the GUI. Libraries such as PyRadiomics, scikit-learn, and pydicom were integrated into the GUI for image loading and feature analysis. Feature extraction and selection using the least absolute shrinkage and selection operator, principal component analysis, multiple regression analysis, and linear discriminant analysis-receiver operating characteristic (ROC) analysis were available. Visualization and comma-separated values format outputs were also supported. The two datasets from The Cancer Imaging Archive were used for validation. Additionally, we compared the analysis time with that of conventional methods (e.g., PyRadiomics, RStudio, and Excel).

Using the developed application, we were able to reduce the analysis time from approximately 20 min to <1/3 of the 6 min required by the conventional method. Additionally, diagnostic support information was obtained through accuracy evaluation, correlation visualization of features, and ROC analysis.

This application improves the efficiency and availability of radiomics analysis and can be easily used by researchers and clinicians with little programming experience. Further enhancements are planned in the future, including the integration of segmentation functions, automatic parameter optimization, and application to other modalities and diseases.