breast 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.

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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

Read More →

Deep learning radiomics in prediction of NAC response

For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that

Read More →

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

In this multi-reader, multi-case study, the authors aimed to investigate whether an artificial intelligence (AI) support system could help to increase the accuracy of breast radiologists reading wide-angle digital breast tomosynthesis (DBT). The study was performed using 240 bilateral DBT exams, with the exams interpreted by 18 radiologists with and without AI support. The authors found that the radiologists improved

Read More →

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Beyond hopes and hype, the clinical applicability of artificial intelligence decision support tools deserves to be rigorously explored in multicenter settings. With this work, we aimed to do so by focusing our attention on solid breast cancer lesions as detected by ultrasound. Indeed, the journey of a patient usually begins with this first-line imaging modality and fast, non-invasive and cost-effective

Read More →

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

In this retrospective study, the authors aimed to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions, thereby avoiding unnecessary biopsies. The authors determined that the investigated automated 4D radiomics approach resulted in an accurate AI classifier that was able to distinguish between benign and malignant lesions, the application of which could

Read More →

The promising possibility of using AI in mammography screening

In two recent publications in European Radiology, we have addressed two, of several, challenges with mammography screening. Firstly, the vast majority of screen exams are normal, which is resource-demanding, especially in the double-reading setting; and secondly, we miss cancer that can be particularly aggressive, later appearing as interval cancer. To understand if artificial intelligence (AI) can identify normal exams, we

Read More →

Artificial Intelligence to Help Radiologists in the Early Detection of Breast Cancer with Mammography and Breast Tomosynthesis

During the COVID-19 pandemic, routine breast cancer screening is largely being put on hold in many countries. Although it is not directly related, Sars-CoV-2 will have an effect on breast cancer screening and care. Having a closer look to Germany for instance, letters inviting women to screening were suspended until April, 30th.* The enormous decline in breast cancer screening is

Read More →

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the prediction of axillary lymph node (ALN) metastasis in breast cancer. The authors found that the CESM-based radiomics nomogram showed good application prospects in the preoperative prediction of ALN metastasis in breast cancer. Key points The CESM-based

Read More →

AI for reading screening mammograms: the need for circumspection

AI is viewed as an emerging technology for reading screening mammograms. However, most studies done so far have adopted retrospective designs that cannot fully appreciate the added value and limitations of AI technologies (Autier et al, Eur Radiol 2020, Apr 21). For instance, these studies cannot inform on numbers and results of biopsies that would have been done following a

Read More →

Using a multiple classifier system for lesion detection and classification in breast MRI analysis

In breast magnetic resonance imaging (MRI) analysis for lesion detection and classification, radiologists agree that both morphological and dynamic features are important to differentiate benign from malignant lesions. This study proposes a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information. The data gained through testing showed that an MCS

Read More →

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

Read More →

Deep learning radiomics in prediction of NAC response

For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that

Read More →

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

In this multi-reader, multi-case study, the authors aimed to investigate whether an artificial intelligence (AI) support system could help to increase the accuracy of breast radiologists reading wide-angle digital breast tomosynthesis (DBT). The study was performed using 240 bilateral DBT exams, with the exams interpreted by 18 radiologists with and without AI support. The authors found that the radiologists improved

Read More →

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Beyond hopes and hype, the clinical applicability of artificial intelligence decision support tools deserves to be rigorously explored in multicenter settings. With this work, we aimed to do so by focusing our attention on solid breast cancer lesions as detected by ultrasound. Indeed, the journey of a patient usually begins with this first-line imaging modality and fast, non-invasive and cost-effective

Read More →

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

In this retrospective study, the authors aimed to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions, thereby avoiding unnecessary biopsies. The authors determined that the investigated automated 4D radiomics approach resulted in an accurate AI classifier that was able to distinguish between benign and malignant lesions, the application of which could

Read More →

The promising possibility of using AI in mammography screening

In two recent publications in European Radiology, we have addressed two, of several, challenges with mammography screening. Firstly, the vast majority of screen exams are normal, which is resource-demanding, especially in the double-reading setting; and secondly, we miss cancer that can be particularly aggressive, later appearing as interval cancer. To understand if artificial intelligence (AI) can identify normal exams, we

Read More →

Artificial Intelligence to Help Radiologists in the Early Detection of Breast Cancer with Mammography and Breast Tomosynthesis

During the COVID-19 pandemic, routine breast cancer screening is largely being put on hold in many countries. Although it is not directly related, Sars-CoV-2 will have an effect on breast cancer screening and care. Having a closer look to Germany for instance, letters inviting women to screening were suspended until April, 30th.* The enormous decline in breast cancer screening is

Read More →

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the prediction of axillary lymph node (ALN) metastasis in breast cancer. The authors found that the CESM-based radiomics nomogram showed good application prospects in the preoperative prediction of ALN metastasis in breast cancer. Key points The CESM-based

Read More →

AI for reading screening mammograms: the need for circumspection

AI is viewed as an emerging technology for reading screening mammograms. However, most studies done so far have adopted retrospective designs that cannot fully appreciate the added value and limitations of AI technologies (Autier et al, Eur Radiol 2020, Apr 21). For instance, these studies cannot inform on numbers and results of biopsies that would have been done following a

Read More →

Using a multiple classifier system for lesion detection and classification in breast MRI analysis

In breast magnetic resonance imaging (MRI) analysis for lesion detection and classification, radiologists agree that both morphological and dynamic features are important to differentiate benign from malignant lesions. This study proposes a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information. The data gained through testing showed that an MCS

Read More →

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