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.

Most used hashtags:

Latest posts

Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

The authors of this retrospective study aimed to evaluate the diagnostic performance of a radiomics model in order to classify hepatic cyst, hemangioma, and metastasis in patients who have been diagnosed with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. The study found that, although inferior to radiologists, the radiomics model was able to achieve substantial diagnostic performance when differentiating

Read More →

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the so-called ‘density mask’ method for emphysema quantification. Since then, many novel methods of quantification, including the assessment of airway wall thickening, air trapping, vascular change and so on, have been introduced, and many studies

Read More →

A decade of radiomics research: are images really data or just patterns in the noise?

Radiomics as a research topic in radiology is certainly a promising field. Over the last years, many publications have shown promising results, showing that image analysis using a radiomics approach could potentially help guide clinical decision making by allowing for accurate, non-invasive diagnosis and prognosis. However, despite the large number of publications, we see only little to no translation of

Read More →

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

The purpose of this study, performed between January 2014 and May 2019 across five different centers, was to construct an MRI radiomics model and help radiologists to improve the preoperative assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC). The authors were able to find that the MRI-based radiomics model could be used to assess the status of

Read More →

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

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 →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

Combining molecular and imaging metrics in cancer: radiogenomics

In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics, proteomics, metabolomics, etc.) to disease treatments and prevention strategies for our patients. Molecular testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. In addition, invasive tumor sampling only provides a snap-shot of often heterogeneous tumors and is not

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

Read More →

Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

The authors of this retrospective study aimed to evaluate the diagnostic performance of a radiomics model in order to classify hepatic cyst, hemangioma, and metastasis in patients who have been diagnosed with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. The study found that, although inferior to radiologists, the radiomics model was able to achieve substantial diagnostic performance when differentiating

Read More →

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the so-called ‘density mask’ method for emphysema quantification. Since then, many novel methods of quantification, including the assessment of airway wall thickening, air trapping, vascular change and so on, have been introduced, and many studies

Read More →

A decade of radiomics research: are images really data or just patterns in the noise?

Radiomics as a research topic in radiology is certainly a promising field. Over the last years, many publications have shown promising results, showing that image analysis using a radiomics approach could potentially help guide clinical decision making by allowing for accurate, non-invasive diagnosis and prognosis. However, despite the large number of publications, we see only little to no translation of

Read More →

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

The purpose of this study, performed between January 2014 and May 2019 across five different centers, was to construct an MRI radiomics model and help radiologists to improve the preoperative assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC). The authors were able to find that the MRI-based radiomics model could be used to assess the status of

Read More →

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

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 →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

Combining molecular and imaging metrics in cancer: radiogenomics

In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics, proteomics, metabolomics, etc.) to disease treatments and prevention strategies for our patients. Molecular testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. In addition, invasive tumor sampling only provides a snap-shot of often heterogeneous tumors and is not

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

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.