breast 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:

Latest posts

Differentiating lesions presenting as calcifications with AI

The authors of this study aimed to evaluate how artificial intelligence computer-aided detection (AI-CAD) differentiates lesions presenting as calcifications, subsequently comparing its performance to that of an experienced breast radiologist. The authors discovered that AI-CAD showed similar diagnostic performances to the radiologists regarding calcifications detected in mammography. Key points Among calcifications with same morphology or BI-RADS assessment, those with positive

Read More →

The potential of texture analysis for breast density classification

Breast cancer continues to be the most commonly diagnosed cancer among women with over 2 million new cases per year worldwide. One important independent risk factor for developing breast cancer is breast density (BD). Epidemiological studies show that women with dense tissue may have an increased risk of developing breast cancer by 2-6 times when compared to women with less

Read More →

Can AI predict breast tumour response?

This proof of concept study examines using a deep learning-based method for the automatic analysis of digital mammograms as a tool to aid in the assessment of neoadjuvant chemotherapy (NACT) treatment response to breast cancer. The authors found that the initial AI performance was able to indicate the potential to aid in clinical decision-making, but in order to continue exploring

Read More →

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning in order to differentiate benign lesions from malignant lesions using model-free parameter maps. The authors determined that radiomics analysis coupled with machine learning does improve the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses

Read More →

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included in the study, with 12 malignant lesions and 33 benign lesions. The study showed how feasible a radiomic approach was in the characterization of enhancing foci on breast MRI. Key points Radiomic signature could distinguish malignant from benign enhancing

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features

Despite the encouraging results, more studies are needed in order to further evaluate these preliminary findings and to find to what extent radiomics and AI approaches can be integrated in clinical practice in a useful and reliable strategy [1]. I think that several issues reduce the application of radiomics approaches in clinical practice: the lack of knowledge of its basic

Read More →

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

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 →

Differentiating lesions presenting as calcifications with AI

The authors of this study aimed to evaluate how artificial intelligence computer-aided detection (AI-CAD) differentiates lesions presenting as calcifications, subsequently comparing its performance to that of an experienced breast radiologist. The authors discovered that AI-CAD showed similar diagnostic performances to the radiologists regarding calcifications detected in mammography. Key points Among calcifications with same morphology or BI-RADS assessment, those with positive

Read More →

The potential of texture analysis for breast density classification

Breast cancer continues to be the most commonly diagnosed cancer among women with over 2 million new cases per year worldwide. One important independent risk factor for developing breast cancer is breast density (BD). Epidemiological studies show that women with dense tissue may have an increased risk of developing breast cancer by 2-6 times when compared to women with less

Read More →

Can AI predict breast tumour response?

This proof of concept study examines using a deep learning-based method for the automatic analysis of digital mammograms as a tool to aid in the assessment of neoadjuvant chemotherapy (NACT) treatment response to breast cancer. The authors found that the initial AI performance was able to indicate the potential to aid in clinical decision-making, but in order to continue exploring

Read More →

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning in order to differentiate benign lesions from malignant lesions using model-free parameter maps. The authors determined that radiomics analysis coupled with machine learning does improve the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses

Read More →

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included in the study, with 12 malignant lesions and 33 benign lesions. The study showed how feasible a radiomic approach was in the characterization of enhancing foci on breast MRI. Key points Radiomic signature could distinguish malignant from benign enhancing

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features

Despite the encouraging results, more studies are needed in order to further evaluate these preliminary findings and to find to what extent radiomics and AI approaches can be integrated in clinical practice in a useful and reliable strategy [1]. I think that several issues reduce the application of radiomics approaches in clinical practice: the lack of knowledge of its basic

Read More →

Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

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 →

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.

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
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • 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.

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
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 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.

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.

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

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
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.