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Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

With the help of radiomics, standard medical images can be transformed into detailed, high-dimensional data sets that go beyond what the eye can see. The typical workflow of radiomic projects involves a series of sequential processes, including image registration, intensity normalization, and segmentation of the region of interest. While there is a general agreement on the essential steps, consensus on

Read More →

Clinical applications of AI and radiomics in neuro-oncology

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Development of MR-based preoperative nomograms predicting DNA copy number subtype in lower grade gliomas with prognostic implication

The aim of this study was to determine the value of MR-based preoperative nomograms in predicting DNA copy number (CN) subtype in patients with lower grade glioma (LGG). The authors achieved this by retrospectively analyzing the overall survival (OS) data of 170 subjects. They found that the shortest OS was observed in patients with CN2 subtype. This preliminary radiogenomics analysis

Read More →

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

Read More →

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade gliomas over clinical isocitrate dehydrogenase (IDH) status. The authors extracted radiomic features from the preoperative MRI data of 296 lower grade glioma patients from their institution as well as The Cancer Genome Atlas (TCGA) and The

Read More →

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly in patients with glioma, as well as to identify potential covariates that may have an influence on the diagnostic performance of machine learning. The authors were able to show that machine learning demonstrated excellent diagnostic performance in

Read More →

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by multi-sequence MRIs. The authors found that DFFM enabled accurate segmentation of CT postoperative gliomas, which may help to improve radiotherapy planning. Key points A fully automated deep learning method was developed to segment postoperative gliomas

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

With the help of radiomics, standard medical images can be transformed into detailed, high-dimensional data sets that go beyond what the eye can see. The typical workflow of radiomic projects involves a series of sequential processes, including image registration, intensity normalization, and segmentation of the region of interest. While there is a general agreement on the essential steps, consensus on

Read More →

Clinical applications of AI and radiomics in neuro-oncology

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Development of MR-based preoperative nomograms predicting DNA copy number subtype in lower grade gliomas with prognostic implication

The aim of this study was to determine the value of MR-based preoperative nomograms in predicting DNA copy number (CN) subtype in patients with lower grade glioma (LGG). The authors achieved this by retrospectively analyzing the overall survival (OS) data of 170 subjects. They found that the shortest OS was observed in patients with CN2 subtype. This preliminary radiogenomics analysis

Read More →

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

Read More →

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade gliomas over clinical isocitrate dehydrogenase (IDH) status. The authors extracted radiomic features from the preoperative MRI data of 296 lower grade glioma patients from their institution as well as The Cancer Genome Atlas (TCGA) and The

Read More →

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly in patients with glioma, as well as to identify potential covariates that may have an influence on the diagnostic performance of machine learning. The authors were able to show that machine learning demonstrated excellent diagnostic performance in

Read More →

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by multi-sequence MRIs. The authors found that DFFM enabled accurate segmentation of CT postoperative gliomas, which may help to improve radiotherapy planning. Key points A fully automated deep learning method was developed to segment postoperative gliomas

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

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