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prostate cancer

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:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyMDCTradiographyworkflow

Latest posts

Does deep learning improve the consistency and performance of radiologists in assessing bi-parametric prostate MRI?

Deniz Alis November 2023

Our study aimed to evaluate whether deep learning (DL) software could enhance the consistency and performance of radiologists in assessing bi-parametric prostate MRI scans. Intriguingly, our findings revealed that the DL software did not significantly improve the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency or the detection performance of clinically significant prostate cancer (csPCa) among radiologists with varying levels

Read More →

The latest developments in radiomics and AI may help against prostate cancer

European Radiology Experimental September 2022

The authors of this systematic review explored the currently available literature on artificial intelligence (AI) and radiomics applied to molecular imaging of prostate cancer. Due to the great promise that nuclear medicine holds regarding improving the quality of life for prostate cancer patients, this study looks at the myriad areas in which AI and radiomics can positively be applied to

Read More →

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Henkjan Huisman January 2022

Prostate MRI can be a game-changer for many men with elevated prostate-specific antigen (PSA). For decades these many men underwent biopsies while never developing prostate cancer. Expert prostate MRI can help avoid these unnecessary biopsies and better target any biopsies. Unfortunately, reading prostate MRI is challenging and time-consuming. Like other medical imaging modalities, AI is explored for helping read prostate

Read More →

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

Anwar Padhani October 2021

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 →

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

David Bonekamp January 2021

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there exists a belief that with infinite training data, an AI system can theoretically be trained that has the ability to handle all possible data and thus be generalised to all environments. Applied to the prostate, this would

Read More →

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

European Radiology October 2020

The purpose of this study was to develop an automatic method for the identification and segmentation of clinically significant prostate cancer in low-risk patients and evaluate this performance in a routine clinical setting. The authors discovered that the proposed deep learning computer-aided method showed promising results in the previously-mentioned identification and segmentation of clinically significant prostate cancer in patients on

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

Machine learning classifiers vs. Experienced radiologists: Predicting Gleason pattern 4 prostate cancer

European Radiology June 2019

The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and classifying breast lesions. To better understand computer-aided segmentation and diagnosis (CAD) features, the authors introduced a data-driven machine learning approach for a CAD system that enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. Key points:

Read More →

Does deep learning improve the consistency and performance of radiologists in assessing bi-parametric prostate MRI?

Deniz Alis November 2023

Our study aimed to evaluate whether deep learning (DL) software could enhance the consistency and performance of radiologists in assessing bi-parametric prostate MRI scans. Intriguingly, our findings revealed that the DL software did not significantly improve the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency or the detection performance of clinically significant prostate cancer (csPCa) among radiologists with varying levels

Read More →

The latest developments in radiomics and AI may help against prostate cancer

European Radiology Experimental September 2022

The authors of this systematic review explored the currently available literature on artificial intelligence (AI) and radiomics applied to molecular imaging of prostate cancer. Due to the great promise that nuclear medicine holds regarding improving the quality of life for prostate cancer patients, this study looks at the myriad areas in which AI and radiomics can positively be applied to

Read More →

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Henkjan Huisman January 2022

Prostate MRI can be a game-changer for many men with elevated prostate-specific antigen (PSA). For decades these many men underwent biopsies while never developing prostate cancer. Expert prostate MRI can help avoid these unnecessary biopsies and better target any biopsies. Unfortunately, reading prostate MRI is challenging and time-consuming. Like other medical imaging modalities, AI is explored for helping read prostate

Read More →

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

Anwar Padhani, Tobias Penzkofer October 2021

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 →

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

David Bonekamp January 2021

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there exists a belief that with infinite training data, an AI system can theoretically be trained that has the ability to handle all possible data and thus be generalised to all environments. Applied to the prostate, this would

Read More →

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

European Radiology October 2020

The purpose of this study was to develop an automatic method for the identification and segmentation of clinically significant prostate cancer in low-risk patients and evaluate this performance in a routine clinical setting. The authors discovered that the proposed deep learning computer-aided method showed promising results in the previously-mentioned identification and segmentation of clinically significant prostate cancer in patients on

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

Machine learning classifiers vs. Experienced radiologists: Predicting Gleason pattern 4 prostate cancer

European Radiology June 2019

The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and classifying breast lesions. To better understand computer-aided segmentation and diagnosis (CAD) features, the authors introduced a data-driven machine learning approach for a CAD system that enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. Key points:

Read More →

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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
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.