multiparametric MRI

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

Predictive potential of intratumoral and multiregion peritumoral radiomics

This study published in Insights into Imaging highlighted the potential of a multiparametric MRI-based radiomic model that integrated intratumoral and peritumoral features as a tool to predict differentiation in hepatocellular carcinoma (HCC). The authors found that the IntraPeri model showed an outstanding ability to predict individualized HCC differentiation through integrating intratumoral and optimal peritumoral features. Key points: Article: Multiparametric MRI-based

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AI for prostate MRI: open datasets, available applications, and grand challenges

This narrative review provides an overview of the current state-of-the-art artificial intelligence (AI) applications for prostate MRI by focusing on open datasets, commercially and publically available AI systems, and challenges. The authors state that large amounts of research are still required in order to successfully utilize AI in the whole prostate pathway. Due to the rapidly growing field, continuous up-to-date

Read More →

MRI-based radiomics to predict response in locally advanced rectal cancer

The study aimed to implement and externally validate an MRI-based radiomics pipeline in order to predict the response to treatment of locally advanced rectal cancer (LARC), while also investigating the impact of manual and automatic segmentations on said radiomics models. The authors were able to show that radiomics models can help clinicians in the prediction of tumor response to chemoradiotherapy

Read More →

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

Read More →

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

The clinical promise of artificial intelligence (AI) in prostate cancer diagnosis has yet to materialize. Any AI application must reach an appropriate level of maturity and robustness for such developments to be accepted by its intended users. Our position paper, “Development of Artificial Intelligence for Precision Diagnosis of Prostate Cancer Using MRI”, co-authored by experts from ESUR and ESUI, elaborated

Read More →

Fully automated pelvic bone segmentation in multiparametric MRI using a 3D convolutional neural network

The accurate skeleton segmentation with their semantic labels represents the initial step to achieve accurate prostate cancer bone metastases detection on DWI and ADC images. Several studies have reported convolutional neural networks (CNNs) for the segmentation of normal bone structures on CT images and bone scans; however, only a few studies on automatic segmentation of normal bone structures on MR

Read More →

Predictive potential of intratumoral and multiregion peritumoral radiomics

This study published in Insights into Imaging highlighted the potential of a multiparametric MRI-based radiomic model that integrated intratumoral and peritumoral features as a tool to predict differentiation in hepatocellular carcinoma (HCC). The authors found that the IntraPeri model showed an outstanding ability to predict individualized HCC differentiation through integrating intratumoral and optimal peritumoral features. Key points: Article: Multiparametric MRI-based

Read More →

AI for prostate MRI: open datasets, available applications, and grand challenges

This narrative review provides an overview of the current state-of-the-art artificial intelligence (AI) applications for prostate MRI by focusing on open datasets, commercially and publically available AI systems, and challenges. The authors state that large amounts of research are still required in order to successfully utilize AI in the whole prostate pathway. Due to the rapidly growing field, continuous up-to-date

Read More →

MRI-based radiomics to predict response in locally advanced rectal cancer

The study aimed to implement and externally validate an MRI-based radiomics pipeline in order to predict the response to treatment of locally advanced rectal cancer (LARC), while also investigating the impact of manual and automatic segmentations on said radiomics models. The authors were able to show that radiomics models can help clinicians in the prediction of tumor response to chemoradiotherapy

Read More →

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

Read More →

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

The clinical promise of artificial intelligence (AI) in prostate cancer diagnosis has yet to materialize. Any AI application must reach an appropriate level of maturity and robustness for such developments to be accepted by its intended users. Our position paper, “Development of Artificial Intelligence for Precision Diagnosis of Prostate Cancer Using MRI”, co-authored by experts from ESUR and ESUI, elaborated

Read More →

Fully automated pelvic bone segmentation in multiparametric MRI using a 3D convolutional neural network

The accurate skeleton segmentation with their semantic labels represents the initial step to achieve accurate prostate cancer bone metastases detection on DWI and ADC images. Several studies have reported convolutional neural networks (CNNs) for the segmentation of normal bone structures on CT images and bone scans; however, only a few studies on automatic segmentation of normal bone structures on MR

Read More →

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  • Option to participate in the European Diploma. 3
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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 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
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04
European Radiology, Insights into Imaging, European Radiology Experimental.