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. 2022 Aug 18;8(8):221.
doi: 10.3390/jimaging8080221.

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

Giovanni Pasini  1 , Fabiano Bini  2 , Giorgio Russo  1 , Albert Comelli  1   3 , Franco Marinozzi  2 , Alessandro Stefano  1
Affiliations

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

Giovanni Pasini et al. J Imaging. .

Abstract

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.

Keywords: CT; MRI; PET; image analysis; machine learning; radiomics; software package.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
matRadiomics Architecture.
Figure 2
Figure 2
MATLAB toolboxes, python modules, and libraries.
Figure 3
Figure 3
Feature selection workflow.
Figure 4
Figure 4
Feature selection result example. Bar plot with scores assigned to features. On the left the legend. Features selected are shown in red.
Figure 5
Figure 5
Example of ROC curves, AUCs, and accuracies.
Figure 6
Figure 6
Example of the total confusion matrix.
Figure 7
Figure 7
PCA results, PC 1: First Principal Component, PC 2: Second Principal Component, EV: Explained Variance.
Figure 8
Figure 8
t-SNE results for different distance measurements. (top left): Euclidean distance, (top right): cityblock distance, (bottom left): chebychev distance, (bottom right): minkowsi distance.
Figure 9
Figure 9
Kruskal-Wallis Table for the PC 1 scores. p-value = 0.6268 > 0.05.
Figure 10
Figure 10
Kruskal-Wallis Table for the PC 2 scores. p-value = 0.1049 > 0.05.
Figure 11
Figure 11
Box plots for the PC1 and PC2 scores.
Figure 12
Figure 12
ROC curves and accuracies examples for the non-harmonized group.
See this image and copyright information in PMC

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