AI Blog

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

Radiomic features of breast parenchyma

The object of this study was to assess the similarities and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. The authors aimed to address the problem using an enlarged set of texture radiomic features, dense/non-dense areas comparison and a new manufacturer, concluding that texture features from FOR PROCESSING mammograms were the

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What do stakeholders think about the future of AI in radiology?

Fear of artificial intelligence (AI) taking over jobs is a view one would think stakeholders would have in the field of radiology, but the authors of this study found that this was not the case. In this review, commentaries, surveys, abstracts, narrative reviews, and a social media study were included from over 60 eligible publications, representing the views of radiologists,

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Single-center versus multi-center biparametric MRI radiomics approach

In this study, the authors’ aim was to investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach used for the discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) through the use of multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets. Using these datasets, the authors were able to determine that a single-center trained radiomics-based bpMRI model

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Do plaque-related factors affect the diagnostic performance of an AI-CADS?

In this study, the authors aimed to investigate the effects of plaque-related factors, if any, on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). This was undertaken by analyzing 1,224 vessels in 306 patients. The authors were able to determine that AI-CADS has the ability to distinguish ≥50% coronary stenosis, but found that an additional manual interpretation

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Precision of MRI radiomics features in the liver and HCC

The aim of this study, consisting of a population of 55 patients who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month, was to assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. The authors determined that MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI

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Multiparametric prostate MRI quality assessment using a PI-QUAL software program

We know that the technical requirements for the acquisition of multiparametric MRI of the prostate have been clearly outlined in the PI-RADS guidelines, but there is still huge variability in image quality among centres across the world. The Prostate Imaging Quality (PI-QUAL) score and its dedicated scoring sheet represent the first attempt to standardise image quality, as they take into

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Can radiomics signatures predict tumor reponse of patients treated with chemotherapy and targeted therapy?

The authors of this retrospective study had the goal of evaluating the effectiveness of radiomics signatures in order to predict the tumor response of non-small cell lung cancer (NSCLC) patients who were treated with first-line chemotherapy, targeted therapy, or a combination of the two. The authors determined that radiomics signatures based on pre-treatment CT scans can accurately predict tumor response

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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

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Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

In this retrospective study, the authors investigated if radiomics features extracted from nephrographic-phase (NP) CT images combined with clinicoradiological characteristics may have the potential to preoperatively differentiate between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). They were able to demonstrate that radiomics analysis may be used as a potentially noninvasive method for distinguishing low- from high-grade

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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
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03
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04
European Radiology, Insights into Imaging, European Radiology Experimental.