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

Radiomics-based prediction of FIGO grade for placenta accreta spectrum

Placenta Accreta Spectrum (PAS) is a serious, life-threatening pregnancy complication. Although rare, as more women are giving birth by Caesarean section, PAS is becoming more common. The most important factor in improving outcomes for mothers and babies is the detection of PAS during pregnancy to ensure the appropriate multi-disciplinary team care is implemented for the pregnancy and birth. However, up

Read More →

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully

Read More →

Are deep models in radiomics performing better than generic models?

In radiomics, deep learning methods are increasingly used because they promise higher predictive performance than models based on generic, hand-crafted features. However, sample sizes in radiomics are often small, and it is known that the performance of deep learning models is often critically dependent on sample size. Therefore, it is unclear whether deep models can outperform generic models. In our

Read More →

QuantImage v2: physician-centered cloud platform for radiomics and machine learning research

The authors of this study proposed a physician-centered vision of radiomics research to help aid the translation of radiomics prediction models into clinical practice and workflow. Free-to-access radiomics tools and frameworks were reviewed to help identify best practices and gaps in the existing software. The authors designed QuantImage V2 (QI2) to help implement this vision and address any issues they

Read More →

A novel AI model to distinguish benign from malignant ovarian tumors

The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors, showing high accuracy and specificity. In coordination with less-experienced radiologists, the model helped in the performance of ovarian tumor assessment, with applications to provide better therapeutic strategies for patients with ovarian tumors. Key points CT-based radiomics and deep

Read More →

Machine learning and radiomics differentiate Crohn’s disease and ulcerative colitis

This retrospective study investigated whether volumetric visceral adipose tissue (VAT) features that were extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approaches are effective when differentiating Crohn’s disease (CD) and ulcerative colitis (UC). The authors concluded that VAT-based deep learning and radiomics features were able to achieve fair accuracy in differentiating CD from UC. Key points High-output feature data

Read More →

T2-weighted MRI-based radiomics discriminati between benign and borderline epithelial ovarian tumors

The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian tumors (EOTs) preoperatively. They were able to show that it can provide critical diagnostic information in the discrimination between benign and borderline EOTs, showing that there is potential to aid personalized treatment options. Key points T2-weighted MRI-based radiomics

Read More →

Utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

The aim of this study was to provide an updated systematic review of radiomics in osteosarcoma, utilizing various databases such as PubMed, Embase, China National Knowledge Infrastructure, and more. Articles found in these databases were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in

Read More →

The effect of preprocessing filters on predictive performance in radiomics

In radiomics, the main goal is to extract quantitative features from medical data to train a predictive model using machine learning techniques. Contrary to statistics, radiomics is data-driven; thus, there is a certain tendency to use as many features as possible. To achieve this, preprocessing filters are often applied; for example, a Gaussian filter smooths the image and thus removes

Read More →

EuSoMII Radiomics Auditing Group Initiative: Review of RQS applications

When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective evaluation of radiomics studies’ scientific/clinical merit. Conceived as a quality seal to be published alongside presented results and newly proposed radiomics models, the RQS has instead mostly been adopted by researchers as a

Read More →

Radiomics-based prediction of FIGO grade for placenta accreta spectrum

Placenta Accreta Spectrum (PAS) is a serious, life-threatening pregnancy complication. Although rare, as more women are giving birth by Caesarean section, PAS is becoming more common. The most important factor in improving outcomes for mothers and babies is the detection of PAS during pregnancy to ensure the appropriate multi-disciplinary team care is implemented for the pregnancy and birth. However, up

Read More →

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully

Read More →

Are deep models in radiomics performing better than generic models?

In radiomics, deep learning methods are increasingly used because they promise higher predictive performance than models based on generic, hand-crafted features. However, sample sizes in radiomics are often small, and it is known that the performance of deep learning models is often critically dependent on sample size. Therefore, it is unclear whether deep models can outperform generic models. In our

Read More →

QuantImage v2: physician-centered cloud platform for radiomics and machine learning research

The authors of this study proposed a physician-centered vision of radiomics research to help aid the translation of radiomics prediction models into clinical practice and workflow. Free-to-access radiomics tools and frameworks were reviewed to help identify best practices and gaps in the existing software. The authors designed QuantImage V2 (QI2) to help implement this vision and address any issues they

Read More →

A novel AI model to distinguish benign from malignant ovarian tumors

The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors, showing high accuracy and specificity. In coordination with less-experienced radiologists, the model helped in the performance of ovarian tumor assessment, with applications to provide better therapeutic strategies for patients with ovarian tumors. Key points CT-based radiomics and deep

Read More →

Machine learning and radiomics differentiate Crohn’s disease and ulcerative colitis

This retrospective study investigated whether volumetric visceral adipose tissue (VAT) features that were extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approaches are effective when differentiating Crohn’s disease (CD) and ulcerative colitis (UC). The authors concluded that VAT-based deep learning and radiomics features were able to achieve fair accuracy in differentiating CD from UC. Key points High-output feature data

Read More →

T2-weighted MRI-based radiomics discriminati between benign and borderline epithelial ovarian tumors

The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian tumors (EOTs) preoperatively. They were able to show that it can provide critical diagnostic information in the discrimination between benign and borderline EOTs, showing that there is potential to aid personalized treatment options. Key points T2-weighted MRI-based radiomics

Read More →

Utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

The aim of this study was to provide an updated systematic review of radiomics in osteosarcoma, utilizing various databases such as PubMed, Embase, China National Knowledge Infrastructure, and more. Articles found in these databases were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in

Read More →

The effect of preprocessing filters on predictive performance in radiomics

In radiomics, the main goal is to extract quantitative features from medical data to train a predictive model using machine learning techniques. Contrary to statistics, radiomics is data-driven; thus, there is a certain tendency to use as many features as possible. To achieve this, preprocessing filters are often applied; for example, a Gaussian filter smooths the image and thus removes

Read More →

EuSoMII Radiomics Auditing Group Initiative: Review of RQS applications

When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective evaluation of radiomics studies’ scientific/clinical merit. Conceived as a quality seal to be published alongside presented results and newly proposed radiomics models, the RQS has instead mostly been adopted by researchers as a

Read More →

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