Cargando…
matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model
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,...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410206/ https://www.ncbi.nlm.nih.gov/pubmed/36005464 http://dx.doi.org/10.3390/jimaging8080221 |
_version_ | 1784775037948002304 |
---|---|
author | Pasini, Giovanni Bini, Fabiano Russo, Giorgio Comelli, Albert Marinozzi, Franco Stefano, Alessandro |
author_facet | Pasini, Giovanni Bini, Fabiano Russo, Giorgio Comelli, Albert Marinozzi, Franco Stefano, Alessandro |
author_sort | Pasini, Giovanni |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9410206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94102062022-08-26 matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model Pasini, Giovanni Bini, Fabiano Russo, Giorgio Comelli, Albert Marinozzi, Franco Stefano, Alessandro J Imaging Article 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. MDPI 2022-08-18 /pmc/articles/PMC9410206/ /pubmed/36005464 http://dx.doi.org/10.3390/jimaging8080221 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pasini, Giovanni Bini, Fabiano Russo, Giorgio Comelli, Albert Marinozzi, Franco Stefano, Alessandro matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title | matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title_full | matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title_fullStr | matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title_full_unstemmed | matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title_short | matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model |
title_sort | matradiomics: a novel and complete radiomics framework, from image visualization to predictive model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410206/ https://www.ncbi.nlm.nih.gov/pubmed/36005464 http://dx.doi.org/10.3390/jimaging8080221 |
work_keys_str_mv | AT pasinigiovanni matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel AT binifabiano matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel AT russogiorgio matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel AT comellialbert matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel AT marinozzifranco matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel AT stefanoalessandro matradiomicsanovelandcompleteradiomicsframeworkfromimagevisualizationtopredictivemodel |