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Visit this page for more information about our journals: European Radiology, Insights into Imaging, and European Radiology Experimental, as well as information about Eurorad. Additionally, if you’re interested in publishing your own work, you can find details here.

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Back to main AI Blog

lung neoplasms

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.

Most used hashtags:

deep learningmachine learningmriradiomicsx-ray computed tomographyinterviewai newsmammographyCOVID-19breast cancerneural networksdiagnostic imagingtomographyworkflowradiographyMDCT

Latest posts

Evaluating Radiomics Research Reporting Assessment Tools to Improve Quality and Generalizability

Hyun Soo Ko April 2024

As computational capabilities in healthcare continue to advance, the realm of texture analysis within medical imaging, known as radiomics, offers a promising avenue for uncovering novel imaging biomarkers aiding precision medicine [1].Nevertheless, clinical translation faces significant hurdles, primarily stemming from the heterogeneity of research questions and inconsistent quality of radiomics reporting, leading to a scarcity of studies that are comparable,

Read More →

The role of radiomics in the detection of lymph node metastases

European Radiology Experimental December 2022

The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung

Read More →

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

European Radiology May 2020

In this study, the authors aimed to develop a deep learning-based artificial intelligence (AI) scheme in order to predict the likelihood of the ground-glass nodule (GGN) being invasive adenocarcinoma that is detected in CT images. The study also compares the accuracy of the AI scheme with the predictions of two radiologists. The study results showed that using an AI scheme

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Evaluating Radiomics Research Reporting Assessment Tools to Improve Quality and Generalizability

Hyun Soo Ko, Kevin Tran April 2024

As computational capabilities in healthcare continue to advance, the realm of texture analysis within medical imaging, known as radiomics, offers a promising avenue for uncovering novel imaging biomarkers aiding precision medicine [1].Nevertheless, clinical translation faces significant hurdles, primarily stemming from the heterogeneity of research questions and inconsistent quality of radiomics reporting, leading to a scarcity of studies that are comparable,

Read More →

The role of radiomics in the detection of lymph node metastases

European Radiology Experimental December 2022

The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung

Read More →

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

European Radiology May 2020

In this study, the authors aimed to develop a deep learning-based artificial intelligence (AI) scheme in order to predict the likelihood of the ground-glass nodule (GGN) being invasive adenocarcinoma that is detected in CT images. The study also compares the accuracy of the AI scheme with the predictions of two radiologists. The study results showed that using an AI scheme

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

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

Reduced registration fees for ECR 2026:
Provided that ESR 2025 membership is activated and approved by August 31, 2025.

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.