4.1 Article

matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model

期刊

JOURNAL OF IMAGING
卷 8, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/jimaging8080221

关键词

radiomics; software package; machine learning; image analysis; PET; MRI; CT

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Radiomics aims to support clinical decisions through its workflow, which includes target identification and segmentation, feature extraction, feature selection, and model fitting. To address the issue of switching between different software, a new free and user-friendly radiomics framework, matRadiomics, was developed to complete the entire radiomics workflow and provide a graphical interface for performance metrics.
Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive-inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.

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