radiomics

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

How machine learning-based high-dimensional CT texture analysis is influenced by segmentation margin

Radiomic workflows include various challenging steps. One of the most demanding steps in radiomics is the segmentation process. Particularly for the renal cell carcinomas, most of the studies used manual tumour contour delineation. In this work, our group wanted to perform an experiment by changing the segmentation margin a little bit, that is, just 2 mm, to see what happens

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Radiomics: a critical step towards integrated healthcare

This article aims to bring together the various technological developments that have taken place in medical imaging analysis and highlight a potential path for the future. While the term “medical image analysis” has classically referred to radiological images (CT, MRI, PET, etc.), we must also remember that digitalization occurred much earlier in other diagnostic disciplines like pathology (with pathomics) and

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Radiomics: the facts and the challenges of image analysis

Radiomics is a complex multi-step process that can be considered as part of the more complex world of Artifical Intelligence (AI). The aim of radiomics is aiding clinical decision-making and outcome prediction for more personalized medicine. Each step of the radiomics process brings challenges that have to be considered; for example, segmentation is challenging because of reproducibility issues. Indeed, there

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Comparing computer- and human-extracted imaging phenotypes

This retrospective study sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Upon obtaining the results, it was possible to conclude that quantitative radiomics of breast cancer may replicate human-extracted tumour size

Read More →

How machine learning-based high-dimensional CT texture analysis is influenced by segmentation margin

Radiomic workflows include various challenging steps. One of the most demanding steps in radiomics is the segmentation process. Particularly for the renal cell carcinomas, most of the studies used manual tumour contour delineation. In this work, our group wanted to perform an experiment by changing the segmentation margin a little bit, that is, just 2 mm, to see what happens

Read More →

Radiomics: a critical step towards integrated healthcare

This article aims to bring together the various technological developments that have taken place in medical imaging analysis and highlight a potential path for the future. While the term “medical image analysis” has classically referred to radiological images (CT, MRI, PET, etc.), we must also remember that digitalization occurred much earlier in other diagnostic disciplines like pathology (with pathomics) and

Read More →

Radiomics: the facts and the challenges of image analysis

Radiomics is a complex multi-step process that can be considered as part of the more complex world of Artifical Intelligence (AI). The aim of radiomics is aiding clinical decision-making and outcome prediction for more personalized medicine. Each step of the radiomics process brings challenges that have to be considered; for example, segmentation is challenging because of reproducibility issues. Indeed, there

Read More →

Comparing computer- and human-extracted imaging phenotypes

This retrospective study sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Upon obtaining the results, it was possible to conclude that quantitative radiomics of breast cancer may replicate human-extracted tumour size

Read More →

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  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
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  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.